├── Config.py ├── Data ├── TrainingData_Keywords.txt ├── TrainingData_Text.txt └── vec5.txt ├── Preprocess.py ├── README.md ├── SC_LSTM_Model.py ├── generation.py └── train.py /Config.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | 3 | class Config(object): 4 | data_dir = 'Data/' 5 | vec_file = 'Data/vec5.txt' 6 | init_scale = 0.04 7 | learning_rate = 0.001 8 | max_grad_norm = 10 #gradient clipping 9 | num_layers = 2 10 | num_steps = 25 #this value is one more than max number of words in sentence 11 | hidden_size = 20 12 | word_embedding_size = 5 13 | max_epoch = 30 14 | max_max_epoch = 80 15 | keep_prob = 0.5 #The probability that each element is kept through dropout layer 16 | lr_decay = 1.0 17 | batch_size = 16 18 | vocab_size = 2423 19 | keyword_min_count = 1 20 | save_freq = 10 #The step (counted by the number of iterations) at which the model is saved to hard disk. 21 | model_path = './Model_News' #the path of model that need to save or load 22 | 23 | # parameter for generation 24 | len_of_generation = 8 #The number of characters by generated 25 | save_time = 80 #load save_time saved models 26 | is_sample = True #true means using sample, if not using argmax 27 | BeamSize = 2 28 | -------------------------------------------------------------------------------- /Data/TrainingData_Keywords.txt: -------------------------------------------------------------------------------- 1 | leaks water plant Japan Radioactive crippled 2 | Google business 3 | FDA menu 4 | Google founder 5 | Robinson Orioles recuperating legend 6 | Rooney Ham United West 7 | medical Medicare worries 8 | Music Industry Cloud Licenses Amazon Will Force Player 9 | health Retiree cost 10 | Seeking a Friend Keira Knightley Steve Carell with 11 | trailer Hangover 12 | neutrino favourite My 13 | World India 14 | breasts launch Christina Hendricks' Vivienne Westwood 15 | Coral BP Oil Spill Finds Damaged USGS 16 | found man charged 13 17 | straight 18 | screen seeing dad Marsden's kids don't 19 | Senate report 20 | economists exceed March China 21 | report Britain Retail banks 22 | Do Genetic Tests 23 | iPad eBay sales 24 | plant construction work nuclear Ohma 25 | Hall of Fame Rodman says in 26 | Page 27 | Chelsea possess attack League Premier best of Ancelotti Carlo 28 | crowds show SeaWorld and orca back seeing Tips 29 | attack Web pages Malicious 30 | made Leadership changes SD Guard 31 | tour June 18 Minneapolis Matthew kick 32 | CDC Birth Rates Biggest in 33 | Social Browser RockMelt Inside 34 | Conscripts 35 | oil fire Alaska bill draws early 36 | Talks Company Hermes Gaultier 37 | David Edelstein Toilet Lincoln Lawyer Issues 38 | City Homer Angels 39 | bankruptcy American Apparel file for warns 40 | Coffee Modernist 41 | constitution provisional Egypt 42 | Pirates Depp 43 | Barbour's horrified wife presidential prospects 44 | Aisle York bridal dresses Isaac Mizrahi exclusively dream New 45 | planned Glencore billion IPO 46 | Party Diane 47 | Photo atlas Japan earthquake 48 | Midwife shortage million lives report says costs 49 | collision disrupted services East Coast after 50 | Express Obama Party Opens 2012 Tea Campaign Against 51 | US Yoga episodes 52 | eyes close woman Meet her 53 | Wikileaks Knockoff Porn Industry Terrified 54 | Birmingham match 55 | trust Cemetery owner tax evasion case Memphis in guilty 56 | criminal damage cautioned Ben Foden England star rugby 57 | Measure 58 | World 59 | from advertisement banned low calorie diet 60 | Look Moment the Holmes Katie 61 | legend unmarked grave Robin Hood buried Brit framer outlaw 62 | Show Teen Betsey Johnson Vogue 63 | Netflix stream new content strikes deal losing others 64 | The TGIF Washington Arts 65 | Dita Von Teese ditches retro shapeless flat wears sex heels dress 66 | oil growth month jobs 67 | captain entire Simon Dhoni World perfection campaign Singh 68 | Some Kids Overscheduled 69 | Parker Vail restaurant deal 70 | interpretations of 1973 Resolution No creative 71 | competition Concern 72 | counts proposes calorie menus 73 | government Jayalalithaa 74 | Gadhafi's Europe dilemma stations 75 | Hydrogen alternative makes process liquid viable petrol behave like gas to than 76 | cocaine took crack prosecutor 77 | Moss Kate hologram magazine tribute McQueen 78 | shot dead near school 79 | Twitter 80 | Pour Syrup 81 | briefing Top evasion the 82 | Robert Laura daughter and 83 | embrace 84 | Insidious classic Movie 85 | Acer slowing pledges rebound 86 | adder 87 | Acer 88 | The Nationals' workout video 89 | Republicans challenge influential seniors 90 | Kelly beat cop NYPD way back 91 | Coast Towns Fall Opposition Strategic Ivory 92 | theaters summer 93 | Delays Action 94 | prison sends faxes ordering escapes TWICE release 95 | Bashar Assad appoints Adel Safar of PM Syria new 96 | falls Jobless 97 | Page CEO Google Larry 98 | Facebook's I Fired Manager 99 | Facebook New Teacher District 100 | Drugmaker drug sharp pregnancy price hike 101 | Balenciaga Evelyne Cristobal Spain Young Museum Politanoff 102 | Unexpectedly Jobless 103 | Facebook Mobile 104 | lowlife bully 105 | charged attempted murder Chloe Man West 106 | Menus Restaurant Proposes Counts 107 | radioactive Moscow 108 | ray of hope Cat allergy sufferers vaccine 109 | areas hit Libyan TV Arrujban Coalition Khoms 110 | finds falling poll Bloomberg ratings Marist Mayor approval College fast 111 | Allay Seattle's How NoSQL Fears 112 | Calderone Insider 113 | Clippers Suns 114 | perjury Defense rests Cuba 115 | search formal complaint Microsoft Google giant's reponse filed 116 | Irresponsible Brown Criticizes Proposed Budget Cuts Scott 117 | Gmail Motion Kinect 118 | US kidnapping of convicted drug trafficker 119 | March in department sales Disaster 120 | Haley Afraid President Wife 121 | MLB inquiry Yankees hand signals 122 | radioactive contamination cases China quarantine watchdog detects 123 | Hall of Fame Rodman says 124 | Gaultier's talking head 125 | Trump Donald 126 | founder ready 127 | Catch Carbon Cells Quickly 128 | Twitter tax San Francisco fracas Proposed 129 | Rockets 130 | UConn NCAA to talk recruit ready Report 131 | QuickBar DickBar Twitter Ditches 132 | reach epidemic liver disease fatty 133 | win carries 134 | Cardinals Holliday 135 | Twitter Francisco city tax 136 | Restaurant Menus FDA Counts 137 | surge tops Verizon February phones Android continues 138 | David Mamet new play Anarchist year London this debut his in 139 | embassy Libyan statement expelled 140 | Qwest Hello Cloud 141 | add economy jobs March 142 | Earth NASA inventions space benefit 143 | to Ireland hit ECB creditors 144 | Half Million Die From Smoking Yearly 145 | Pistons retires 146 | fight forced parents tried guard 147 | Capsules 148 | release Karan basics essential redux line 149 | Doesn't Have Jimenez 150 | admits strangling death women 151 | 40 fraud charges 152 | Perry Gaga accused 153 | celebrity big Unveiled weddings 154 | Color Bathing Mobile Entirely New Light 155 | iPhone Camera Inadvertently Revealed 156 | Reliance 157 | Forgiveness sins 158 | Terrorists Deadly Troops Pollution Air Iraq As as Affects 159 | goal swearing Manchester United West Ham apologises outburst Wayne Rooney 160 | Gadhafi's protection EU Libyan civilians ouster says 161 | Giants Dodgers lose again 162 | Lowly Coyotes shootout in Avs beat 163 | tournament 164 | jobs 165 | death killer sentenced Swaziland serial 166 | in Marlins ace Mets debut 167 | d'Ivoire Cote exercise restraint parties 168 | Jewish clashes Group claiming police 169 | jobs added Employment report April 170 | YouTube App Windows Microsoft Blames Google Phone 7 Suckiness 171 | Notable amp Quotable 172 | frees rule 173 | Twilight Cast 174 | buddy Kutcher Ashton Bieber Justin film worst 175 | The Music Never Stopped 176 | The Bush Doctrine Obama 177 | Barbour's Haley 178 | slaying suspect Illinois prosecutor won't pursue death penalty 179 | Department State 180 | Moves Museum Gehry LVMH Forward Art 181 | rally speech to the Politics Weekly 182 | our Syrup the 183 | memorial national protect resources China facilities martyr launches 184 | southeast explosions witness Tripoli Air raid 185 | severed arm found lake Arrest after 186 | alarming Specialists 187 | health NH man's overhaul Federal court 188 | NJ probation pleading guilty charged sex lesser offense child man in gets to case after 189 | journey India cricketers complete exultant Mumbai 190 | bankruptcy Dov 191 | Boeing 192 | skirt 193 | basketball Memphis NCAA took 6 60 Tigers Larry dies Former 194 | Twitter Tax San Francisco Row 195 | National Guard Reisch takes SD commander 196 | cat allergies cure injection Simple 197 | Explain Genetic Tests 198 | hopes England Attwood stamping 199 | valuation Bilt India better issue shelves 200 | Thinker Richard Feynman 201 | Penelope Cruz Hollywood star 202 | Lowe Pearl 203 | Gloucestershire arrested gardening 204 | Facebook Teacher New Jersey Suspends 205 | Work Employers Labor Numbers 206 | Bachmann 207 | Northampton Sharks match 208 | affect hormones Toxins baby food 209 | Nets Sixers 210 | week comedy 211 | stalled peace concern stresses Israeli premier Ban 212 | superfood ranks syrup Maple 213 | Fame star Hollywood Penelope Cruz Walk 214 | week's 215 | Unemployment drop boosts 216 | explosives site Olympic a London 217 | Facing Budget Deficit Shifts Costs Cities 218 | Cancer 219 | quoting Media Matters Accurately Democratic Congressman constitutes 220 | Porn Fined Condoms 221 | backfire takes aim 222 | House force law budget bill 223 | mobile payments Amazon report suggests dabble 224 | Sharapova title Azarenka 2nd Miami defeats 225 | World Cup final Sri Lanka India victory over reward superb effort team 226 | syrup researchers superfood 227 | US 228 | proposes calorie counts 229 | Medicare Federal 230 | Quotes 231 | Continue 232 | looks Season characters Meter quirkier Idol 233 | confusion budget ECZ Acronyms 234 | bear 235 | Arizonans Brewer 236 | Avert Shutdown Plan Agreement 237 | YouTube Windows Microsoft Google Phone 238 | Gorman dead Boston GM Former 239 | disease life Institute Study span 240 | Republicans scuttle Palin's oil tax increase 241 | US Judge question 242 | Clippers missing Steve Suns despite rout 243 | Nico Steenkamp drinking Rotherham 244 | Michelle Oldies But Goodies Obama 245 | Fashions Friend 246 | Men Mad Seasons Will Creator Says 247 | Unemployment boosts stocks drop 248 | New York Huffington Paywalls Jousting Times Smurfs 249 | West Ham Rooney Manchester United Rallies Outscores Victory 250 | Winner Competes City 251 | religion of God is alive state 252 | Lane Lois 253 | clean up act Girlfriend helps 254 | video Pakistan floods Australia 255 | Crimesider official Obama Austria 256 | balance leaves Sri Lanka India World Cup final Mahela Jayawardene 257 | Naval Academy Student Fake Weed Claims 258 | killed robbery officer suspect hospital responding shot call 259 | House Mimic Effort 260 | diabetes heart 261 | Anu Raina garments colour debut clothing graphic 262 | Portugal rescue ready Europe 263 | Zimbabwe paper New daily 264 | bikini amazing body Van Outen Denise flaunting outfit 265 | crack cocaine Vegas Bruno Mars cases Paris Hilton arrest Deputy DA 266 | Sunday news shows Guest lineups the 267 | Barnes Matt Lakers suspended medicine 268 | United 4 Rooney West Ham 269 | keep therapy kangaroo Crimesider Okla wants woman disabled 270 | modified milk 271 | Maya Moore Wade Trophy 272 | Myanmar Merkel Imbau Bebaskan 273 | technical launch aborted hitch Ariane 274 | suspended future calling Teacher criminals students 275 | United Scores Goals 276 | guide Metronomy round English Riviera 277 | Dodgers payroll increase 278 | in The Addams Family to Brooke Shields Broadway Celebrity Circuit 279 | Metro East State tests radiation 280 | shows the Sunday news lineups 281 | Windows Phone Unimportance 7 the Apps 282 | Party Blocks Implementation 283 | Jupiter rings caused comet 284 | Northern policeman bomb 285 | Shopping Bargains Great Deals Coupon Codes Weekend 286 | New Life Art of 287 | Shoe Index 288 | Spanish as sly fax 289 | shooting Glover Henry New Orleans burning 290 | fight arrested crossing guard tried parents in beating a 291 | Tea Party 292 | Manager Facebook's 293 | AIDS infections goal zero report 294 | Clouds 295 | recession sales soar immune Silk shirts 296 | Senate approves spending caps 297 | Risks Radiation Benefits Cancer Therapy Outweigh Second 298 | kidnapped 299 | economy growth 300 | Twitter account James Franco deletes 301 | cops house Best friend 302 | total of America falls Q1 top underwriter UPDATE muni 303 | Vegas Markoff girl recalls with Philip call 304 | tax break San Francisco 305 | Gaga 306 | First Speech Protests Leader Syria's 307 | hand Yankees signals 308 | Used 309 | Google China tax investigation 310 | Rivers promises Wilkins 311 | iPhone 3D Camera Next 312 | VCU Cinderellas Butler After Final Four 313 | spending Mad hiatus Men 314 | painkillers prevent melanoma 315 | participation Jobless unemployment 316 | Vegetarians lower diabetes heart risk 317 | Kraton Ground Plastics 318 | ooze Undersea volcanoes explode 319 | Native American fashion 320 | life alien Searching 321 | Appointee Who Criminalize Sex Outside Marriage Pushback Sean Parnell Faces 322 | Dolce and Gabbana trial 1bn 323 | near Ivory palace Fighting rages president's 324 | Life premiere Tree 325 | Unemployment 326 | 2 4 match report West Ham United Manchester 327 | ceasefire Libyan gov't rebels turns down offer by 328 | vending counts machines eat at the cinema guilt free everywhere from bakeries Calorie but don't worry 329 | Mars Connection 330 | US for reform crackdown Assad Syria condemns 331 | BP well coral found old 332 | CEO 333 | April Five Stories Fools' Jokes 334 | shortage frontline doctors spread could medicine 335 | State TV Ivory Coast Palace 336 | NHL game 337 | Twitter San Francisco tax deal 338 | win 339 | fraud India charges 40 340 | Mossad's secret operation 341 | Potbellied men blind go more likely to 342 | waiting mauled transplant Charla woman face 343 | man soldier murder NIreland acquitted 344 | story Union Correction 345 | menu Calorie counting movie theaters 346 | million 347 | seek rate bailout Ireland 348 | economy jobs 349 | surprisingly slimmer Sweet 350 | Skewed priorities 351 | transgender health of study gay 352 | privacy bug UberSocial fixes 353 | Building Blown Up South Pole's 354 | Google tax China investigation 355 | milk radiation Fukushima Washington State 356 | pregnant Emily child Deschanel 357 | Jon fan Stewart Obama's Libya speech 358 | operation tumour president 359 | serial killer Swaziland hanging to death Judge 360 | Penelope Lace Cruz Hudgens Karolina Violet Kurkova Vanessa 361 | MYLEENE KLASS SHOWS NEWBORN HERO 362 | seven killings guilty Westside Rapist 363 | conflict of interest Director 364 | 18 look stylish Plus size clothes 365 | Minnesota lands 366 | new film events 367 | Dolce Gabbana 368 | Disappearing 369 | Earth gravity 370 | Future of Cinema James Cameron the 371 | Fame Hollywood star Walk 372 | Cablevision Optimum streams hundreds TV iPad VOD app 373 | Phillies win to desire 374 | arrested German stadium plot 375 | Solar Power Venture Agreement Soitec Electric Schneider for Reaches 376 | playmaker Adel Taarabt Chelsea the QPR 377 | CEO Sony iPhone 5 Accidentally 378 | Buble Michael Lopilato TV 379 | south Philippines demands 380 | Curious Georges 381 | faucet clean 382 | plant Japan official ocean crippled nuclear highly radioactive water leaking from 383 | Omagh New double insult 384 | opens beauty Lagerfeld Beauty EcoTools 385 | Hyundai Shows Blue Fuel Cell Concept Delays Hybrid Models 386 | Absorbs Cost Rises Misses Estimates 387 | swimsuit acts slimsuit shape 388 | Guest Sunday news the lineup 389 | teenage Wales 390 | interviewed Young Edwards 391 | Peters challenges State US 392 | Ireland says Portugal needs bailout 393 | Emissions New Trade Created Toxic Hotspots 394 | Syrian government form president appoints 395 | 3-Day Phestival Phish Phavors New York Phinger Lakes 396 | Gmail Google Motion 397 | Senate Heinrich Mexico run 398 | Bobby Heedles unforgivable says friend George Wong Officer corpse 399 | join cast Chloe Moretz 400 | calories Popping 401 | DVD 402 | World 403 | Swaziland sentenced serial death killer 404 | the Twitter tribes 405 | Mexico killed 406 | Chinese rival ZTE Sweden's Ericsson 407 | Victims Abuse at Home 408 | Senate 409 | iPad Warner Cable 410 | World Better 411 | workout Broncos 412 | Dozens Injured Abu Dhabi Crash 413 | Chernobyl of 414 | killed Israeli airstrike Three 415 | Reisch National commander 416 | daring figure Nicky Hilton denim shorts lace panel vest top slashed 417 | stake Delphi GM sells for 418 | Well Right Dior 419 | Rainn Wilson 2011 Apr Theaters Ellen Page become heroes 420 | Fashion for life girls 421 | Sleeping Beauty True 422 | Barbara 423 | Cruz Penelope Hollywood Blvd 424 | Calipari sticking with approach 425 | Vodafone Essar 426 | resistance Entrenched for Ivory Coast leader calls 427 | in Nashville country 428 | Week Ahead Buy Stocks Not Market 429 | Palace Tibet 430 | Phillies Game Day Astros 5 4 431 | comments Facebook teacher's remove children parents 432 | Android Google to carriers flexibility 433 | policeman bomb and 434 | Baines Everton Darren Villa saves day Toffees brace 435 | Cancer Prostate Screening Doesn't Cut Death Rates 436 | Japanese dog survival saga 437 | Police identify 438 | glad Mad Men 439 | Family who woman IV contaminated 440 | registry replaces fishing licenses Free 441 | Google Privacy Winners Group 442 | split Liam months Miley Cyrus Hemsworth 443 | serial killer death hanging sentences Swaziland 444 | Budget Cuts Latest Showdown Head 445 | kissed Pippa Middleton designer bridesmaid Royal dress her 446 | Hot Pants 447 | Azarenka Sony Ericsson Open Maria Sharapova Victoria 448 | unflattering Jovovich Milla jeans 449 | calorie FDA menu counts 450 | Now enters the Chewbacca mighty 451 | Click Magazines 452 | sharp Drugmaker pregnancy drug price 453 | family Early Show Stylish rainwear 454 | Qwest Line dead completes acquisition 455 | Washington Weekend Trunk Shows 456 | Franco PhD 457 | evasion cemetery guilty Man pleads tax 458 | Reveals Undercover 459 | Morrison Matthew Glee summer tour 460 | Gillard 461 | Speed Google Page 462 | GOP Launching Attack Senior Citizens 463 | multiple women father 464 | Democracy 465 | sugar soaring blood coffee 466 | Street vogue Wall 467 | Larsson Craig justice 468 | iPhone Verizon bump 469 | Mulvey Nassau hat exit Mangano 470 | pride winegrowers shrug plastic French bottle 471 | Google Motion Digits Comic April Fools Sans 472 | coal mine accident trapped China's Xinjiang Ten underground 473 | hairstyles Ben Barnes humiliating 474 | Germans Nasdaq bid snatch from 475 | Seattle Investigate Excessive Use Force Feds Police 476 | Christie Presidential Contender 477 | million websites infects LizaMoon 478 | radiation Metro 479 | women kids multiple fathers 480 | FDA 481 | Italy Create Strategic Investment Vehicle 482 | soaring coffee blood 483 | Senate report 484 | -------------------------------------------------------------------------------- /Data/TrainingData_Text.txt: -------------------------------------------------------------------------------- 1 | Radioactive water leaks from crippled Japan plant 2 | Why Google is getting into the ""Like"" business 3 | FDA proposes calorie counts on menus 4 | Google founder hopes to prove he's ready to lead 5 | Orioles legend Robinson recuperating 6 | Rooney hat-trick as United down West Ham 7 | High-end medical option prompts Medicare worries 8 | Music Industry Will Force Licenses on Amazon Cloud Player — or Else 9 | Retiree health cost estimate falls, for a change 10 | Steve Carell ""Seeking a Friend"" with Keira Knightley 11 | The NEW "The Hangover Part 2" trailer 12 | My favourite particle: the neutrino 13 | India player ratings against Sri Lanka in Cricket World Cup 2011 final 14 | Christina Hendricks' shows off breasts at Vivienne Westwood launch 15 | USGS Finds 2,000-year-old Coral Near BP Oil Spill: Was it Damaged? 16 | SC man charged after 13 dogs found wild in home 17 | Hawks stop Celtics for 4th straight win 18 | Marsden's kids don't like seeing dad on screen 19 | Senate report on subprime mess due soon: report 20 | China's March CPI rise to exceed 5%: economists 21 | Retail banks in Britain stung in report 22 | Do At-Home Genetic Tests Tell Too Much and Explain Too Little? 23 | eBay releases iPad 2 sales 24 | J-Power halts construction work on Ohma nuclear plant 25 | Rodman says he's in Hall of Fame 26 | Larry Page set to take over as Google CEO starting Monday 27 | Chelsea possess best attack of Premier League: Carlo Ancelotti 28 | Tili's back at SeaWorld and so are the crowds: Tips on seeing the orca show 29 | Malicious attack hits a million Web pages 30 | Leadership changes made in SD Guard 31 | "Glee's" Mr. Schue (Matthew Morrison) to kick off tour in Minneapolis on June 18 32 | CDC: Drop in U.S. Birth Rates Biggest in 30 Years 33 | RockMelt Rethinks Web Browser With More Social Inside 34 | CBS Conscripts Twittering Stars for #CBSTweetWeek 35 | Alaska oil bill draws early fire 36 | Hermes in Talks to Sell Stake in Jean-Paul Gaultier Company 37 | David Edelstein Talks Lincoln Lawyer , Rand Paul’s Toilet Issues 38 | Ka'aihue Homer Powers Kansas City Past Angels 2-1 39 | American Apparel warns it may file for bankruptcy 40 | Ristretto | Modernist Coffee 41 | Egypt introduces provisional constitution 42 | Depp could be back for more Pirates 43 | Barbour's wife ""horrified"" at presidential prospects 44 | Isaac Mizrahi bridal dream dresses exclusively for The Aisle New York 45 | Glencore gets HK nod for planned $10 billion IPO 46 | Diane Gives a Party 47 | Photo atlas: The Japan earthquake 48 | Midwife shortage costs over a million lives, report says 49 | East Coast services disrupted after collision 50 | Tea Party Express Opens 2012 Campaign Against Obama 51 | Yoga halves irregular-heartbeat episodes -US study 52 | Meet the woman who can't close her eyes 53 | The Wikileaks Knockoff That Has the Porn Industry Terrified 54 | Birmingham City 2 Bolton Wanderers 1: match report 55 | Cemetery owner pleads guilty in Memphis to tax evasion in trust fund case 56 | England rugby star Ben Foden cautioned for criminal damage 57 | The Measure 58 | In the World 59 | Ultra low calorie diet advertisement is banned from TV 60 | Look of the Moment | Katie Holmes 61 | Brit framer buried in unmarked grave is outlaw behind Robin Hood legend? 62 | Betsey Johnson, Karlie Kloss Show Teen Vogue Their Prom Pics (PHOTOS) 63 | Netflix strikes deal to stream new content while losing others 64 | Arts Post It: TGIF for D&G; The Other Sheen Comes to Washington 65 | Dita Von Teese ditches sex siren wears for shapeless retro dress and flat heels 66 | Crude oil at 30-month high on jobs growth, weak $US 67 | Simon Hughes: India captain Mahendra Singh Dhoni timed entire World Cup campaign to perfection 68 | Are Some Kids Overscheduled? 69 | Parker: Elway, partners huddle on Vail restaurant deal 70 | No to creative interpretations of Resolution 1973 71 | Concern over competition in banking 72 | FDA proposes calorie counts on menus 73 | Root out family-centred government, says Jayalalithaa 74 | Gadhafi's gas stations pose dilemma for Europe 75 | Hydrogen ""to become a viable alternative to petrol"" thanks to new process that makes gas behave like liquid 76 | High-profile drug prosecutor who took on Bruno Mars and Paris Hilton resigns after being caught ""buying crack cocaine"" 77 | Kate Moss back in the hologram dress as she pays tribute to Alexander McQueen in magazine shoot 78 | 4 shot dead near school 79 | Twitter tax deal creates classic San Francisco row 80 | Pour on the Syrup - It's Good For You 81 | Top evasion from the briefing 82 | Robert Marshall-Andrews and his daughter Laura 83 | French embrace ""le vin plastique’ 84 | Movie review: A classic haunted-house tale in ""Insidious"" 85 | Acer pledges efforts to rebound amid slowing sales 86 | Why we must make the adder count 87 | Acer pledges efforts to rebound amid slowing sales 88 | The Nationals' workout video 89 | Republicans challenge influential seniors group 90 | NYPD Comm. Kelly: A beat cop from way back 91 | Strategic Ivory Coast Towns Fall To The Opposition 92 | Word of Mouth: What will hit, miss and surprise in theaters this summer 93 | FDA Panel Delays Action on Dyes Used in Foods 94 | Man escapes prison TWICE after his wife sends faxes ordering his release 95 | Bashar Assad appoints Adel Safar new PM of Syria 96 | Jobless rate falls to 8.8% 97 | Larry Page set to take over as Google CEO starting Monday 98 | Facebook's Fired Manager: I Did Nothing Wrong 99 | New Jersey District Suspends Teacher Over Facebook Post 100 | Drugmaker scales back sharp hike in price of pregnancy drug 101 | Evelyne Politanoff: Balenciaga And Spain, The Art Of Cristobal Balenciaga At de Young Museum 102 | U.S. Jobless Rate Unexpectedly Drops to Two-Year Low in March 103 | Facebook Launches New Mobile Site 104 | ""NEVER! He is a lowlife bully"" 105 | Man charged with attempted murder of Chloe West 106 | FDA Proposes Calorie Counts On Restaurant Menus 107 | Japan's 'radioactive particles' in Moscow 108 | Cat allergy sufferers given ray of hope with new vaccine 109 | Coalition hit areas in Khoms, Arrujban -Libyan TV 110 | Mayor Bloomberg's approval ratings are falling fast, Marist College poll finds 111 | How NoSQL Is Helping Allay Seattle's Radiation Fears 112 | The Insider | Athena Calderone 113 | Suns 111, Clippers 98 114 | Defense rests in Cuban ex-CIA agent's perjury case 115 | Why Microsoft filed a formal complaint about Google and the search giant's reponse 116 | Scott Brown Criticizes Proposed Budget Cuts As ""Irresponsible"" 117 | Gmail Motion April Fools' gag inevitably turned into reality using Kinect (video) 118 | 2 convicted of kidnapping slain US drug trafficker 119 | Disaster takes toll on auto, department store sales in March 120 | Haley Barbour's Wife Is Afraid He'll Be President 121 | Yankees to stop hand signals after MLB inquiry 122 | China quarantine watchdog detects 10 radioactive contamination cases 123 | Dennis Rodman says he's headed for Hall of Fame 124 | Jean Paul Gaultier's talking head 125 | Donald Trump Lands a Fox News Gig 126 | Google founder hopes to prove he's ready to lead 127 | New Device Uses Carbon Nanotubes to Catch Tumor Cells Quickly 128 | Proposed tax break to keep Twitter in city leads to classic San Francisco fracas 129 | Rockets bombard slumping Spurs 130 | Report: Former UConn recruit ready to talk to NCAA 131 | Twitter Ditches QuickBar A.K.A. ""DickBar"" 132 | Non-alcoholic fatty liver disease to reach epidemic status in U.S. 133 | Five-run rally carries Cubs to win 134 | Cardinals hold off on putting Holliday on DL 135 | Proposed tax break to keep Twitter in city leads to classic San Francisco fracas 136 | FDA Proposes Calorie Counts On Restaurant Menus 137 | Verizon iPhone tops in cell phones in February, but Android continues to surge 138 | David Mamet to debut his new play ""The Anarchist"" in London this year 139 | William Hague statement: Diplomats expelled from Libyan embassy in London 140 | Bye, Bye Qwest and Hello Cloud! 141 | U.S. economy adds 216,000 jobs in March 142 | NASA space inventions benefit all our lives on Earth 143 | Ireland wants to hit bank creditors, ECB says no 144 | Half a Million Die From Smoking Yearly in U.S. 145 | Pistons retires Rodman's No.10 jersey 146 | The worst parents in America? Couple ""forced their son, 7, to fight with rival then beat up patrol guard who tried to stop it"" 147 | AL Capsules 148 | Donna Karan to release redux line of seven essential luxe basics 149 | Jimenez Doesn't Have His Usual Stuff 150 | 'Westside Rapist' admits strangling seven women to death, real number may be up to 30 151 | India charges ex-minister for $40b fraud 152 | Katy Perry accused of copying Lady Gaga who is accused ripping off Madonna! 153 | Unveiled: Thirty years of big, fat celebrity weddings 154 | True Colors: Bathing Mobile In An Entirely New Light 155 | Sony CEO May Have Inadvertently Revealed iPhone 5""s 8MP Camera 156 | UPDATE 1-India ex-minister, Reliance ADA charged in telecoms graft case 157 | Forgiveness of sins 158 | Air Pollution Affects Troops in Iraq: As Deadly as Terrorists? 159 | Manchester United's Wayne Rooney apologises for swearing outburst after hat-trick goal against West Ham 160 | EU president says Gadhafi's ouster would be best protection for Libyan civilians 161 | Giants lose to Dodgers again, 4-3 162 | Lowly Avs beat Coyotes in shootout 4-3 163 | NCAA tournament: 164 | Surge in jobs fuels economic optimism 165 | Swaziland serial killer sentenced to death 166 | Marlins ace shuts down Mets in debut 167 | U.S. calls on all parties in Cote d'Ivoire to exercise restraint 168 | Ethiopia: Group claiming to be Jewish clashes with police 169 | Employment report no April Fools Day joke as 216,000 jobs added 170 | Microsoft Blames Google for the Suckiness of the YouTube App on Windows Phone 7 171 | Notable & Quotable 172 | Parole rule frees two after 17 years 173 | Cast in B.C. for ""Twilight"" wedding scene 174 | Justin Bieber and Ashton Kutcher: the worst buddy film ever? 175 | The Music Never Stopped 176 | The Obama Doctrine v. The Bush Doctrine 177 | Will Haley Barbour's Wife Let Him Run for President? 178 | Illinois prosecutor won't pursue death penalty for slaying suspect 179 | State Department Builds A Panic Button App 180 | LVMH Moves Forward With Gehry Art Museum 181 | Politics Weekly podcast: Ed Milband's speech to the anti-cuts rally 182 | Pour on the Syrup - It's Good For You 183 | China launches national survey on ""red resources"" to better protect martyr memorial facilities 184 | Air raid on Tripoli, explosions in southeast suburb: witness 185 | Arrest after severed arm found in lake 186 | Specialists say stats on self-injury are alarming 187 | Federal court tosses NH man's health overhaul suit 188 | NJ man charged in child sex case gets probation after pleading guilty to lesser offense 189 | India cricketers complete epic journey in exultant Mumbai 190 | Dov denies bankruptcy 191 | India Orders More Boeing Maritime Planes 192 | How to dress: The return of the calf-length skirt 193 | Former Memphis basketball coach Larry Finch, who took Tigers to 6 NCAA tournaments, dies at 60 194 | Twitter Tax Deal Creates Classic San Francisco Row 195 | Reisch takes over as commander of SD National Guard 196 | Simple injection could cure cat allergies 197 | Do At-Home Genetic Tests Tell Too Much and Explain Too Little? 198 | Disgraced Dave Attwood hopes England will still come calling after ban for stamping 199 | Bilt shelves LSE issue, sees better valuation in India 200 | Richard Feynman, the Thinker 201 | Penelope Cruz gets star on Hollywood Boulevard 202 | The Saturday interview: Pearl and Daisy Lowe 203 | Gloucestershire man arrested for 'naked gardening' 204 | New Jersey District Suspends Teacher Over Facebook Post 205 | Getting Back to Work: U.S. Employers Hiring, Labor Department Numbers Show 206 | Michele Bachmann Ambitions Could Lead To ""Rocky Crusade"" In 2012 207 | Northampton Saints 53 Sale Sharks 24: match report 208 | Toxins in baby food might affect hormones: study 209 | Sixers 115, Nets 90 210 | This week's new live comedy 211 | Ban stresses concern over stalled peace talks in phone call with Israeli premier 212 | Maple syrup joins ranks of broccoli and blueberries as new ""one-stop shop"" superfood 213 | Penelope Cruz gets Hollywood Walk of Fame star 214 | This week's new live music 215 | Unemployment drop boosts stocks 216 | London 2012: Third arrest over explosives found in a car at Olympic site 217 | Facing Budget Deficit, Ariz. Shifts Costs To Cities 218 | U.S. Cancer Rates Continue to Fall 219 | Media Matters: Accurately quoting Democratic Congressman constitutes a 'smear' 220 | Hustler Fined $14G for Not Using Condoms in Porn 221 | GOP takes aim at AARP: Will it backfire? 222 | House passes ""force of law"" budget bill 223 | Amazon set to dabble in mobile payments, report suggests 224 | Azarenka defeats Sharapova for 2nd Miami title 225 | Geoffrey Boycott: India's World Cup final victory over Sri Lanka fitting reward for a superb team effort 226 | Maple syrup a ""superfood"": researchers 227 | US jobless rate hits two-year low 228 | FDA proposes calorie counts on menus 229 | Federal Plan Would Streamline Medicare 230 | Quotes of the day 231 | U.S. Cancer Rates Continue to Fall 232 | Idol Meter looks at Season 10""s ""quirkier characters"" 233 | EPA? ECZ? Acronyms cause confusion over budget deal 234 | Virus may have killed polar bear 235 | Jan Brewer Offers To Restore Life Saving Transplant Program In Exchange For Taking Health Care Away From 160,000 Arizonans 236 | No Agreement on Plan to Avert Shutdown 237 | Microsoft Blames Google for the Suckiness of the YouTube App on Windows Phone 7 238 | Former Boston GM Lou Gorman dead at 82 239 | Study by Bay Area's Buck Institute blazes trail for life span extension and disease treatments 240 | Republicans scuttle Palin's oil tax increase 241 | Judge: Ex-Somali leader in US can be questioned under oath in suit alleging rights abuses 242 | Suns rout Clippers 111-98 despite missing Steve Nash 243 | Rotherham forward Nico Steenkamp suspended for banned stimulant after drinking energy supplement 244 | Michelle Obama Brings Back Some Oldies But Goodies (PHOTOS) 245 | Canine Couture: Fashions for Your Four-Legged Friend 246 | ""Mad Men"" Creator Says 7 Seasons Will Be Enough 247 | Unemployment drop boosts stocks 248 | Huffington Post, New York Times Now Jousting Over Paywalls, Smurfs 249 | Rooney Outscores West Ham as Manchester United Rallies for Victory 250 | A City Winner Now Competes Against the World 251 | God is alive, and likely frustrated with the state of religion 252 | Former Lois Lane Kidder Praises Adams Casting 253 | Girlfriend helps Wood clean up act 254 | When the floods came: Australia and Pakistan - video 255 | ""Obama"" robber nabbed, say Austrian officials - Crimesider 256 | India v Sri Lanka: Mahela Jayawardene century leaves World Cup final in balance 257 | Fake Weed Claims Another Naval Academy Student 258 | Tenn. police officer is shot and killed responding to robbery call; suspect in hospital 259 | House Talks Mimic ""Gang of Six"" Effort 260 | Vegetarians may be at lower diabetes, heart risk 261 | Hand-dyed graphic garments colour debut of clothing line from Anu Raina 262 | Europe rescue fund says ready to help Portugal - paper 263 | New daily paper for Zimbabwe 264 | Denise Van Outen steps out in odd outfit days after flaunting her amazing post-baby bikini body 265 | Deputy DA in Vegas who handled Paris Hilton, Bruno Mars cases quits after crack cocaine arrest 266 | Guest lineups for the Sunday news shows 267 | Lakers' Matt Barnes takes his medicine after being suspended 268 | West Ham 2 Man United 4: Rooney bags a hat-trick leaves title in sight 269 | Okla. woman wants to keep disabled kangaroo as therapy pet - Crimesider 270 | Genetically modified cows produce ""human"" milk 271 | UConn’s Maya Moore wins 3rd Wade Trophy 272 | Merkel Imbau Myanmar Bebaskan Tahanan Politik 273 | Ariane 5 launch aborted due to technical hitch 274 | Teacher suspended for calling her students ""future criminals"" 275 | Rooney Scores 3 Goals for United; Chelsea Slips 276 | Metronomy guide us around The English Riviera 277 | Dodgers' opening-day payroll increases 278 | Brooke Shields to return to Broadway in ""The Addams Family"" - Celebrity Circuit 279 | State tests for radiation in Metro East 280 | Guest lineups for the Sunday news shows 281 | Windows Phone 7 and the Unimportance of Apps 282 | Tea Party Blocks Health-Care Implementation 283 | Ripples in Saturn and Jupiter rings ""caused by comet crashes"" 284 | Booby-trap bomb kills Northern Ireland policeman 285 | Weekend Shopping Bargains: Check Out These Great Deals and Coupon Codes 286 | New Life for the Art of Lace-Making 287 | Consumer Shoe Index 288 | Spanish jailbird is as sly as a fax 289 | 2 New Orleans cops sentenced in post-Katrina shooting & burning of Henry Glover 290 | 2 Michigan parents arrested in beating a 73-year-old crossing guard who tried to end fight 291 | Tea Party 292 | Facebook's Fired Manager: I Did Nothing Wrong 293 | New AIDS report sets zero new infections goal 294 | Clouds over Amazon.com 295 | Silk shirts prove immune to recession as sales soar 296 | NH Senate approves town spending caps 297 | Benefits of Radiation Therapy Outweigh Risks of a Second Cancer: Study 298 | 16 Agusan teachers, students kidnapped 299 | Job growth lifts outlook on economy 300 | James Franco deletes his Twitter account 301 | Best friend leads cops to pot house 302 | UPDATE 1-Bank of America top Q1 muni underwriter; total falls 303 | Vegas call girl recalls run-in with Philip Markoff 304 | Proposed tax break to keep Twitter in city leads to classic San Francisco fracas 305 | Chic in Review | Dear Lady Gaga … 306 | Syria's Leader Defiant In First Speech Since Protests 307 | Yankees will stop hand signals 308 | FDA Panel Delays Action on Dyes Used in Foods 309 | Google faces tax investigation by China 310 | Rivers promises to rib Wilkins for dust-up with ex-ref 311 | Will the Next iPhone Have a 3D Camera? 312 | After Final Four berths, can we still call Butler, VCU Cinderellas? 313 | How the stars of ""Mad Men"" are spending their hiatus 314 | Can painkillers prevent melanoma? 315 | Jobless rate down to 8.8% as unemployment hits a two-year low, but labor participation still down 316 | Vegetarians may be at lower diabetes, heart risk 317 | Get In On The Ground Floor Of Plastics With Kraton 318 | Undersea volcanoes don't just ooze, they also explode 319 | Fall 2011 fashion week goes Native American 320 | Searching for alien life? Check out failed stars 321 | Sean Parnell Appointee Who Wants To Criminalize Sex Outside Of Marriage Faces Pushback 322 | Dolce and Gabbana will not face trial after £1bn fraud case is thrown out 323 | Ivory Coast: Fighting rages near president's palace 324 | The Tree of Life to premiere in UK 325 | Unemployment rate falls to two-year low 326 | West Ham United 2 Manchester United 4: match report 327 | Libyan gov't turns down ceasefire offer by rebels 328 | Calorie counts for everywhere from bakeries to vending machines (but don't worry, you can eat at the cinema guilt free) 329 | The Mars Connection 330 | US condemns Syria crackdown, presses Assad for reform 331 | 2,000-year-old coral found near BP well site 332 | Doubts follow new Google CEO Larry Page 333 | Five Stories We Wish Were April Fools' Jokes 334 | Concierge medicine fills a niche, but its spread could worsen shortage of frontline doctors 335 | Ivory Coast Rebels Seize State TV, Control Gbagbo Palace 336 | NHL game results, April 1 337 | Twitter tax deal creates classic San Francisco row 338 | AL champ Texas rallies for 9-5 win over Boston 339 | India charges ex-minister for $40b fraud 340 | Mossad's not so secret operation 341 | Potbellied men more likely to go blind 342 | Charla Nash, woman mauled by chimp, waiting for face transplant (video) 343 | NIreland man acquitted over 1977 murder of soldier 344 | Correction: Ohio Union Fight story 345 | Calorie counting on menus to exempt movie theaters 346 | $9 million bequest offers ‘stability’ 347 | Ireland to seek rate cut for EU bailout next week: Report 348 | U.S. economy adds 216,000 jobs 349 | Sweet! Candy eaters surprisingly slimmer 350 | Skewed priorities 351 | Report urges study of gay, transgender health 352 | UberSocial fixes privacy bug 353 | South Pole's First Building Blown Up After 53 Years 354 | Google faces tax investigation by China 355 | Fukushima radiation in Washington State milk and water 356 | 'Bones' Star Emily Deschanel pregnant with first child 357 | Jon Stewart not a fan of Obama's Libya speech 358 | Slovenian president undergoes operation of prostate tumour 359 | Judge sentences Swaziland serial killer to death by hanging 360 | Violet and Lace: Penelope Cruz, Vanessa Hudgens, Karolina Kurkova 361 | MYLEENE KLASS SHOWS OFF HER NEWBORN HERO 362 | ""Westside Rapist"" pleads guilty to seven killings 363 | ‘Miral’: Director has conflict of interest 364 | Plus size fashion: Size 18 clothes CAN look stylish 365 | ""Snow-bate"" lands new film ""Lumpy"" in Minnesota 366 | This week's new film events 367 | Dolce and Gabbana will not face trial after £1bn fraud case is thrown out 368 | The Amazing Disappearing Neutrino 369 | The Geoid: Why a map of Earth's gravity yields a potato-shaped planet 370 | James Cameron and the Future of Cinema 371 | Penelope Cruz gets Hollywood Walk of Fame star 372 | Cablevision Optimum for iPad app now available, streams hundreds of TV channels plus VOD 373 | Phillies display a desire to win 374 | German arrested in stadium bomb plot 375 | Schneider Electric Reaches Agreement With Soitec for Solar Power Venture 376 | Chelsea enter the race for £8m QPR playmaker Adel Taarabt 377 | Sony CEO Accidentally Reveals Secret Details about iPhone 5 378 | Singer Michael Buble weds TV star Luisana Lopilato 379 | Gunmen in south Philippines kidnap 16, set demands 380 | Curious About Georges 381 | Hands-free faucet not so clean 382 | Japan official says highly radioactive water is leaking from crippled nuclear plant into ocean 383 | New Omagh attack ""a double insult"" 384 | Beauty agenda: EcoTools adds e-commerce, Karl Lagerfeld opens beauty pop-up 385 | Hyundai Shows Off Blue 2 Fuel Cell Concept, Delays Two Hybrid Models 386 | H&M Profit Misses Estimates as Retailer Absorbs Cost Rises 387 | Now, get back to shape with the swimsuit that acts as a slimsuit! 388 | Guest lineups for the Sunday news shows 389 | Morning-after pill free for teenage girls in Wales 390 | Andrew Young interviewed again in Edwards case 391 | State Rep. Knollenberg challenges US Rep. Peters 392 | WRAPUP 3-Fitch says Portugal needs bailout, S&P cuts Ireland 393 | Has Emissions Cap and Trade Created Toxic Hotspots? A New Study Says No 394 | Syrian president appoints ex-minister to form government 395 | Phish Phavors New York With 3-Day Phestival In Phinger Lakes 396 | Google prank 2011: Gmail Motion 397 | Heinrich to run for Senate in New Mexico 398 | ME's corpse grab of Officer George Wong 'unforgivable' says friend, Officer Bobby Heedles 399 | 'Dark Shadows' update: Chloe Moretz in talks to join cast 400 | Popping off on calories 401 | This week's new DVD & Blu-ray 402 | In the World 403 | Swaziland serial killer sentenced to death 404 | The rise of the Twitter tribes 405 | Young boy among 4 killed at Mexico burrito stand 406 | Sweden's Ericsson suing Chinese rival ZTE 407 | Music Stars in Public, Victims of Abuse at Home 408 | You can't deem-and-pass without the Senate 409 | Time Warner Cable boosts iPad app channel lineup 410 | Movie Review - ""In A Better World"" - 411 | Broncos top brass skip the workout of D-lineman Bowers 412 | Dozens Injured in 50-Car Crash in Abu Dhabi 413 | Radioactive boars in Germany a legacy of Chernobyl 414 | Three killed in Israeli airstrike 415 | Reisch takes over as commander of SD National Guard 416 | Nicky Hilton shows off her figure in daring lace panel vest top and slashed denim shorts 417 | GM sells Delphi stake for $3.8B 418 | Dior Will Replace Galliano Whenever They Darn Well Please, All Right? 419 | Apr. 1, 2011 - Now In Theaters: Rainn Wilson & Ellen Page become "Super" heroes 420 | Fashion for life: Why ethics girls have the best style 421 | True Grit star Hailee Steinfeld in talks to play Sleeping Beauty 422 | Dim Bulb: Sen. Barbara Boxer, D-Calif. 423 | Corrected: Penelope Cruz gets star on Hollywood Blvd 424 | Calipari sticking with one-and-done approach 425 | Vodafone lifts Essar stake by 33% 426 | Entrenched Ivory Coast leader calls for resistance 427 | It's not all country in Nashville 428 | The Week Ahead: Buy Stocks, Not The Market 429 | Tibet through the Lenses-Potala Palace 430 | Game of the Day / Phillies 5, Astros 4 431 | Facebook comments prompt parents to remove children from teacher's class 432 | Google to limit carriers' Android flexibility. Good. 433 | Car bomb kills N. Ireland policeman 434 | Everton 2-2 Aston Villa: Leighton Baines saves the day for Toffees after brace from Darren Bent 435 | Prostate Cancer Screening Doesn't Cut Death Rates: Study 436 | Japanese dog: survival saga 437 | Police identify man killed in pursuit 438 | ""Mad Men"" creator now glad man 439 | Family of woman who died after contaminated IV sues 440 | Free registry replaces fishing licenses 441 | Winners in Google Buzz Suit: ACLU and YMCA, Not Privacy Group - Digits 442 | Back on? Miley Cyrus and Liam Hemsworth have been 'hanging out a lot' five months after split 443 | Judge sentences Swaziland serial killer to death by hanging 444 | Budget Cuts Head To Latest Showdown 445 | How Royal bridesmaid Pippa Middleton kissed goodbye to all the secrecy over her dress designer 446 | Hot Pants 447 | Maria Sharapova beaten in final of Sony Ericsson Open by inspired Victoria Azarenka 448 | Milla Jovovich steps out in unflattering high-waisted jeans 449 | FDA proposes calorie counts on menus 450 | Now enters the mighty Chewbacca! 451 | Click to Buy Vogue Bag as Magazines Become E-Tailers 452 | Drugmaker scales back sharp hike in price of pregnancy drug 453 | Stylish rainwear for the whole family - The Early Show 454 | Line goes dead for Qwest as CenturyLink completes acquisition 455 | A Weekend of Washington Trunk Shows 456 | James Franco, earnest in D.C., on quitting Twitter, getting his PhD 457 | Man pleads guilty to tax evasion in cemetery case 458 | Undercover Audio Reveals Rajaratnam's Frantic Calls After Goldman-Buffett Tip 459 | ""Glee"" star Matthew Morrison announces summer tour 460 | Gillard wakes up to long-term threat 461 | How Fast Is Your Site? Measure It With Google’s Page Speed Online 462 | GOP Launching Multi-Pronged Attack On Senior Citizens 463 | Many women have kids with multiple fathers 464 | Democracy after all 465 | Fast-food + coffee = soaring blood sugar 466 | Dividends are back in vogue on Wall Street 467 | Craig ""will do Larsson justice"" 468 | Where's that Verizon iPhone bump? 469 | Upon exit, Nassau's Mulvey tips hat to Mangano 470 | French winegrowers shrug off pride, bottle in plastic 471 | April Fools in Tech Land: Comic Sans and Google Motion - Digits 472 | Ten trapped underground in coal mine accident in China's Xinjiang 473 | Ben Barnes' humiliating hairstyles 474 | Nasdaq, ICE bid to snatch NYSE from Germans 475 | Feds Investigate Seattle Police for Possible Excessive Use of Force 476 | Chris Christie A Strong 2012 Presidential Contender: Poll 477 | LizaMoon attack infects millions of websites 478 | State tests for radiation in Metro East 479 | Many women have kids with multiple fathers 480 | FDA Proposes Calorie Counts on Menus 481 | Italy May Create Strategic Investment Vehicle 482 | Fast-food + coffee = soaring blood sugar 483 | US Senate report on subprime mess due soon-report 484 | -------------------------------------------------------------------------------- /Data/vec5.txt: -------------------------------------------------------------------------------- 1 | 2423 5 2 | 0.068038 0.031822 -0.099307 0.038730 0.027102 3 | in -0.051625 -0.063918 -0.132715 -0.122302 -0.265347 4 | to 0.052796 0.076153 0.014475 0.096910 -0.045046 5 | for 0.051237 -0.102637 0.049363 0.096058 -0.010658 6 | of 0.073245 -0.061590 -0.079189 -0.095731 -0.026899 7 | the -0.063727 -0.070157 -0.014622 -0.022271 -0.078383 8 | on -0.035222 0.008236 -0.044824 0.075308 0.076621 9 | and 0.038209 0.012271 0.063058 0.042883 -0.124830 10 | a -0.060385 -0.018999 -0.034195 -0.086732 -0.025636 11 | The 0.007047 -0.091152 -0.042944 -0.068369 -0.072737 12 | after -0.015879 0.062852 0.015722 0.061325 -0.099242 13 | as 0.009263 0.037517 0.028697 -0.010072 -0.013621 14 | Google -0.028538 0.055254 -0.005006 -0.052552 -0.045671 15 | New 0.002533 0.063183 0.070852 0.042174 0.077393 16 | with 0.087201 -0.038249 -0.041059 0.086816 0.068579 17 | at 0.082778 0.043505 -0.087001 0.044570 0.037580 18 | over 0.022163 -0.033666 0.039190 0.053745 -0.035787 19 | new 0.043216 0.015423 -0.062604 0.080569 -0.048067 20 | - -0.082845 0.071703 0.023919 0.007112 -0.143654 21 | from 0.020938 -0.093300 0.058466 0.043800 -0.072198 22 | is -0.097202 0.084112 -0.039420 -0.079256 0.082824 23 | report -0.056238 0.075111 0.027172 0.002640 -0.082924 24 | says -0.083282 -0.095505 -0.088210 -0.055905 -0.011998 25 | U.S. 0.058011 0.075739 0.041408 0.036887 0.032945 26 | tax -0.069971 -0.041650 -0.090858 -0.018628 0.057368 27 | by -0.024618 0.015948 -0.085570 0.054084 0.053853 28 | FDA -0.077264 -0.042614 -0.061859 0.012588 -0.000540 29 | In -0.084707 0.075229 -0.052739 -0.095430 0.038077 30 | Twitter 0.098950 0.043028 -0.080341 -0.032113 -0.075664 31 | India -0.059184 0.017656 -0.103558 0.073629 -0.095965 32 | 2 0.002586 -0.055312 -0.009769 -0.013941 0.089312 33 | back 0.063224 -0.034452 -0.092420 -0.103802 0.030066 34 | who 0.008077 -0.050621 -0.054815 -0.084373 -0.106788 35 | case -0.045362 -0.086125 -0.098274 -0.024498 -0.066119 36 | star 0.016924 -0.044474 0.057899 -0.110540 -0.075852 37 | A 0.003916 0.062362 0.022698 -0.036460 -0.102871 38 | World -0.009555 -0.027423 -0.001667 0.059044 -0.014035 39 | off 0.089990 0.072621 -0.080395 0.063147 -0.088493 40 | be -0.044186 -0.012130 0.004121 -0.089998 -0.103929 41 | out 0.049578 -0.105261 -0.098487 -0.099974 -0.066507 42 | his 0.005349 -0.085235 0.003128 -0.074313 0.017653 43 | West 0.051237 -0.014411 -0.055456 -0.100599 -0.031042 44 | Will -0.021761 0.078800 0.058376 0.012276 0.084859 45 | man 0.077848 0.068035 0.096601 -0.027035 0.068480 46 | With -0.048544 -0.053954 -0.087216 0.032668 -0.064174 47 | gets -0.030422 -0.040280 -0.070351 0.012227 0.080272 48 | classic 0.056448 0.009543 -0.093391 -0.081211 0.003580 49 | San 0.083136 0.030096 0.063309 0.002011 0.033247 50 | Francisco -0.085678 0.077906 -0.032909 -0.009651 -0.065636 51 | To 0.083447 -0.010977 0.048399 0.038428 -0.008251 52 | US -0.002194 0.018004 0.008096 -0.068991 0.035293 53 | State -0.084613 0.065119 0.027410 0.015927 -0.077484 54 | not -0.065073 -0.025949 -0.018568 -0.053183 0.028433 55 | Why 0.029875 0.067936 0.048935 -0.003986 -0.017521 56 | calorie 0.078047 0.048841 -0.026387 -0.044781 0.073175 57 | counts -0.030309 0.051629 0.001648 0.034556 0.014157 58 | menus -0.087172 0.005945 0.080447 -0.038170 -0.027090 59 | Rooney -0.096367 -0.089043 0.013418 0.068890 -0.019571 60 | United -0.013689 0.021574 0.042295 0.040748 -0.053801 61 | down -0.093241 -0.067204 -0.116343 0.018018 -0.024979 62 | Ham -0.055709 0.033364 0.005928 0.046070 -0.012879 63 | shows 0.090305 0.078972 -0.056073 -0.071198 -0.043278 64 | Senate 0.078687 0.019970 -0.086766 0.028750 -0.064241 65 | sales 0.067799 -0.095751 0.074487 0.071947 0.039953 66 | Page -0.048502 -0.085914 -0.040667 0.014794 0.021475 67 | CEO 0.006429 -0.088087 -0.098753 0.043432 0.018853 68 | are -0.027461 0.068489 -0.002586 -0.068943 0.039074 69 | may 0.052653 -0.006609 0.068451 -0.019317 -0.076608 70 | her -0.013617 -0.143373 -0.115494 -0.013776 -0.021490 71 | deal 0.033545 -0.030057 -0.089757 -0.046883 0.067812 72 | jobs 0.005376 0.001059 0.078871 -0.012929 -0.024793 73 | will -0.092016 -0.082273 0.037919 0.047913 -0.014520 74 | rate -0.018377 0.043455 -0.054808 -0.054517 -0.078756 75 | Calorie 0.084277 -0.017958 0.040931 -0.097653 -0.031597 76 | How 0.084412 0.082377 0.032864 -0.086795 -0.051752 77 | April 0.042017 -0.089270 -0.023581 0.093813 -0.005160 78 | takes -0.045541 0.040308 0.066538 0.009798 -0.056834 79 | iPhone 0.044149 0.003123 0.049046 -0.060347 0.078976 80 | all 0.023885 0.034939 -0.061213 -0.007942 -0.085950 81 | Ireland -0.029190 0.027357 -0.059101 -0.091904 -0.005060 82 | death 0.035832 -0.087454 0.014145 0.089243 0.003516 83 | Penelope -0.072711 -0.026575 0.059254 0.035423 -0.108987 84 | have -0.014550 -0.014016 0.064176 0.019001 0.006933 85 | killed 0.067321 -0.013766 -0.105035 -0.067513 -0.067635 86 | proposes 0.065734 -0.035407 0.022646 -0.060344 0.004504 87 | he's -0.035689 -0.055153 -0.053497 0.081600 -0.004503 88 | ready -0.057624 -0.043458 0.005595 0.079920 0.063478 89 | Cup 0.075360 0.064015 0.000364 -0.003344 0.068278 90 | final -0.029928 -0.041724 0.059789 0.054146 0.080682 91 | charged 0.037569 0.099427 -0.030515 0.025615 -0.053427 92 | found 0.087119 -0.089257 -0.003621 -0.071580 -0.037166 93 | stop 0.016862 0.099975 -0.025512 0.002419 -0.020317 94 | win 0.028916 0.075124 0.013889 0.054056 -0.000233 95 | March 0.069461 0.049238 0.025578 0.067147 0.030582 96 | Too -0.010774 -0.092415 -0.045190 -0.020861 -0.073763 97 | Fame 0.084024 0.094786 0.076854 -0.028075 -0.067484 98 | Larry -0.021979 0.060493 0.022699 0.010468 -0.055614 99 | set 0.061612 0.082541 0.059209 0.013578 0.086189 100 | attack 0.029926 -0.026424 0.031252 0.042901 -0.065475 101 | | 0.078785 0.059634 0.057631 -0.068584 -0.084179 102 | Party -0.003459 -0.045233 0.069727 0.068656 0.057489 103 | Coast -0.058169 0.088051 0.025453 -0.060296 0.033013 104 | woman 0.086803 0.071517 -0.005603 0.073589 -0.009261 105 | guilty 0.080185 -0.087398 -0.073538 -0.080163 -0.105489 106 | TV -0.076942 -0.048901 -0.047617 -0.067484 -0.111876 107 | drug 0.070159 0.070814 -0.060724 -0.096250 0.071546 108 | Ivory 0.022756 -0.079760 0.003055 -0.088799 -0.010529 109 | Fall -0.013609 -0.067269 -0.054411 0.066359 -0.015740 110 | Man 0.022004 -0.016933 -0.099186 -0.050361 0.069040 111 | Facebook -0.044326 0.080583 0.059019 0.018293 0.050803 112 | On 0.051688 0.043028 -0.026881 0.061090 0.048113 113 | Proposed -0.038508 0.004458 0.018649 -0.055697 -0.017814 114 | China -0.013010 -0.034938 0.024825 -0.040572 0.056067 115 | keep -0.065655 -0.055735 -0.049330 -0.005010 -0.016329 116 | leads -0.063590 -0.085860 0.029287 -0.009215 0.042410 117 | Former -0.047399 0.030012 -0.053030 -0.004142 -0.037745 118 | up 0.011289 0.001772 0.003300 -0.017804 -0.098098 119 | fraud -0.021491 -0.036219 -0.068991 -0.035810 0.004027 120 | Swaziland -0.069639 -0.005201 0.043188 -0.108954 -0.075252 121 | serial 0.073922 0.038991 -0.057146 -0.078209 0.036273 122 | killer -0.082966 -0.029636 0.009845 0.093909 0.061943 123 | 7 -0.077760 0.025635 -0.043327 -0.068183 0.019579 124 | & -0.008744 0.039238 0.023120 0.028046 -0.069419 125 | Cruz 0.052095 -0.042685 -0.070956 0.053873 -0.022281 126 | Hollywood 0.096950 -0.020877 0.085805 -0.096673 -0.036376 127 | This 0.084371 0.065207 0.091634 -0.083746 -0.004833 128 | week's 0.036160 -0.049570 -0.095199 -0.075007 0.008069 129 | Study -0.023362 -0.059538 0.012775 0.014082 0.016915 130 | Now 0.044148 0.040079 0.045480 0.019666 -0.036278 131 | water -0.026267 0.046514 0.054873 0.083639 0.080237 132 | Japan -0.041679 -0.092010 -0.021179 0.050649 0.044730 133 | plant -0.053582 -0.090909 0.024764 0.001387 -0.048123 134 | into 0.013685 0.032378 -0.024567 -0.077523 0.015397 135 | hopes -0.051176 0.095087 0.085002 0.034459 -0.032739 136 | prove -0.072464 -0.078015 0.051851 0.020564 -0.049387 137 | hat-trick 0.046442 -0.073845 0.054523 -0.021155 0.055552 138 | Music -0.078459 0.060102 -0.079469 0.036752 -0.062733 139 | health 0.058544 -0.050563 -0.040498 0.038779 0.058495 140 | Sri -0.068562 -0.045459 -0.100874 -0.056705 -0.071277 141 | 2011 0.048825 -0.040298 -0.067292 0.094778 -0.071069 142 | it -0.085094 -0.059787 -0.073997 -0.039026 0.022793 143 | kids 0.090021 -0.080932 -0.013336 -0.080441 -0.081054 144 | due -0.089193 0.064391 0.051117 -0.060449 0.010541 145 | iPad -0.008527 -0.019981 0.027086 0.006303 -0.058953 146 | Chelsea -0.067401 -0.030412 0.040090 0.017957 0.015229 147 | best -0.051988 0.078879 -0.019362 0.030005 -0.043694 148 | so 0.059806 -0.063388 -0.099905 -0.082767 0.045023 149 | hits 0.046559 0.075701 -0.053366 -0.098770 0.021470 150 | million 0.027484 -0.051831 0.016099 -0.100458 0.061713 151 | SD 0.091712 0.042308 0.052837 -0.078915 -0.111423 152 | Guard 0.025667 0.024436 0.003997 -0.066579 -0.011393 153 | Rates 0.051947 0.032863 -0.064562 -0.099955 0.046543 154 | oil -0.082403 -0.020111 -0.082847 -0.036884 0.030247 155 | Talks -0.074421 -0.004587 -0.049552 0.078086 0.068253 156 | City 0.004157 0.091093 -0.035425 0.055295 -0.076543 157 | could -0.088457 0.046031 -0.108264 -0.041369 0.075972 158 | York -0.085010 -0.033885 -0.052797 0.097309 -0.024418 159 | East -0.062107 -0.059424 -0.028590 0.079533 0.033429 160 | Tea -0.008849 0.095768 0.013816 0.077358 0.011109 161 | 2012 0.060699 -0.089364 -0.105521 0.021159 0.045761 162 | Obama 0.014989 0.089297 -0.072821 0.064344 0.014753 163 | study 0.048117 -0.096764 0.094780 0.030297 -0.047407 164 | match -0.091833 -0.075298 0.025647 -0.068503 -0.088634 165 | pleads -0.068928 0.034172 0.009645 0.094142 -0.057770 166 | evasion 0.098746 -0.096625 0.081717 -0.035668 0.023409 167 | low -0.058584 -0.046606 -0.019512 0.052411 -0.026182 168 | Show 0.075792 0.055654 -0.037932 -0.027056 -0.109811 169 | Post -0.074906 0.074581 -0.088020 -0.094692 0.083425 170 | Washington 0.099109 0.006612 0.045167 -0.007638 -0.066708 171 | dress -0.093287 -0.099833 -0.053775 0.005140 -0.115436 172 | No 0.074108 -0.066078 -0.055816 -0.099984 -0.087063 173 | that -0.087500 0.053403 0.075128 0.058258 0.067655 174 | Mars -0.104433 -0.071037 0.041200 0.032331 -0.054044 175 | 4 0.071784 0.067059 0.079818 -0.078236 -0.023712 176 | dead 0.014483 -0.055838 0.013861 -0.034669 -0.074012 177 | near -0.067316 0.005843 -0.009872 -0.045174 0.069377 178 | It's -0.069384 0.071411 -0.027614 0.063723 0.069686 179 | For -0.024093 -0.000414 0.022334 -0.006185 -0.107835 180 | You 0.079863 -0.016408 0.040740 -0.044155 -0.002210 181 | beat 0.067087 -0.068845 0.095572 0.070451 0.055197 182 | Delays -0.037124 -0.072874 -0.025405 0.017814 -0.116715 183 | Jobless 0.006124 -0.078144 -0.050602 -0.005645 -0.096247 184 | falls 0.013157 0.062558 -0.068047 0.069353 -0.002666 185 | Teacher -0.013871 0.028969 0.001119 0.086898 0.037578 186 | Over 0.037477 0.023233 0.005429 -0.024043 0.089314 187 | Art -0.081670 0.053280 -0.003655 0.023409 0.026170 188 | Of 0.002125 -0.014747 0.007763 -0.046182 0.029782 189 | Young 0.045313 0.066520 0.039048 -0.038729 -0.061563 190 | Proposes -0.039645 0.096184 0.067782 0.003567 -0.041941 191 | Counts 0.096723 -0.013758 -0.075834 -0.073279 -0.052938 192 | Menus 0.011951 -0.096122 0.049650 -0.104512 -0.077855 193 | Is 0.075434 0.079411 0.088553 -0.037519 0.064868 194 | Microsoft 0.049562 -0.003434 -0.075736 0.060881 0.018785 195 | Budget 0.022573 -0.065356 -0.020495 -0.069966 -0.049650 196 | Motion -0.097322 0.007722 -0.004634 -0.047632 -0.093205 197 | break 0.048867 -0.003757 -0.095488 -0.076798 -0.056104 198 | city 0.064257 -0.008416 0.063011 0.082553 0.032673 199 | fracas 0.085075 -0.020849 0.068194 0.033412 -0.072010 200 | NCAA 0.075098 -0.031244 -0.083923 -0.070467 -0.079689 201 | but 0.063399 -0.106908 0.002814 -0.026343 0.069784 202 | debut 0.094916 -0.005898 -0.071594 -0.055255 -0.039347 203 | London 0.047019 -0.000887 -0.043260 -0.072921 -0.067774 204 | Libyan 0.079879 0.053501 -0.018202 -0.062653 0.053021 205 | economy 0.093417 0.022329 0.076380 0.080762 0.016223 206 | adds 0.027513 -0.013743 -0.057300 -0.039394 0.042606 207 | 216,000 -0.002562 -0.080139 -0.086056 0.018462 -0.090514 208 | parents -0.073645 -0.071065 -0.033868 -0.067837 -0.019833 209 | seven 0.063269 -0.023910 0.027366 -0.066854 0.037063 210 | Have 0.077872 0.022259 0.042100 -0.081449 -0.081120 211 | women -0.085420 0.035873 -0.078523 0.004590 -0.083663 212 | ex-minister 0.027471 0.009167 0.027987 -0.094064 -0.002393 213 | Sony -0.053820 0.063321 -0.082714 0.027801 -0.101372 214 | Manchester 0.025896 0.060925 -0.024742 0.099477 -0.066129 215 | president -0.055726 -0.021776 0.062408 0.078288 -0.091056 216 | sentenced 0.039016 0.010293 0.036694 0.036136 0.075193 217 | App -0.037881 -0.021343 0.073919 -0.024297 -0.089376 218 | Windows -0.034245 -0.064879 -0.036535 0.004472 0.040816 219 | Phone -0.054868 -0.099609 -0.053930 0.089829 0.016742 220 | film -0.088760 -0.042750 0.020297 0.042749 -0.034877 221 | still 0.022315 -0.078865 -0.074077 -0.094680 -0.118413 222 | arrested -0.063171 -0.023006 -0.035274 -0.083449 0.060645 223 | Back 0.025882 -0.030912 -0.020156 0.040969 0.005243 224 | talks -0.004164 -0.089409 -0.080848 -0.030540 0.063274 225 | call 0.090232 0.084149 -0.002836 0.078532 -0.013860 226 | Unemployment -0.045435 0.017655 0.062753 0.075311 0.077688 227 | boosts 0.005421 0.024284 0.019139 0.031916 -0.116365 228 | Cancer 0.044154 -0.011222 0.008533 -0.092406 0.046855 229 | Not -0.078575 -0.104750 0.018238 0.050144 -0.009435 230 | two-year -0.006592 -0.085001 -0.005802 -0.032214 -0.067453 231 | Life 0.017062 0.051987 0.019081 -0.070733 0.062250 232 | can 0.081082 -0.095374 -0.071603 -0.060273 -0.013425 233 | suspended -0.014214 0.010009 0.023846 0.014761 -0.008487 234 | ""Mad -0.037372 -0.038669 0.079961 -0.037895 -0.094968 235 | Men"" 0.069537 0.079723 0.029807 -0.014986 -0.026790 236 | Guest -0.013298 -0.050728 0.014219 0.008658 -0.066100 237 | lineups -0.101890 0.067365 -0.061440 0.030962 0.079128 238 | Sunday -0.031011 0.099575 0.043009 -0.097106 -0.012351 239 | news 0.015852 -0.008137 0.088189 0.049847 0.041808 240 | radiation 0.065819 -0.052537 0.067296 0.063052 0.022925 241 | bomb -0.055316 0.005251 -0.026249 -0.089497 -0.018035 242 | James 0.029709 -0.080490 -0.094506 -0.048913 0.054345 243 | top -0.012145 0.022364 -0.018241 0.054962 -0.116738 244 | After -0.009688 -0.018496 -0.002885 0.052855 0.002089 245 | face -0.039009 0.092174 -0.010292 -0.045590 -0.096255 246 | Radioactive 0.039133 -0.062780 -0.039214 0.050958 -0.062434 247 | crippled -0.065038 -0.096704 -0.047616 0.000741 0.011606 248 | getting 0.022580 -0.036779 -0.041325 0.017626 -0.101762 249 | founder -0.091827 -0.018851 0.032004 -0.038333 0.095151 250 | lead -0.018027 -0.077121 0.009697 -0.000835 -0.050466 251 | Medicare 0.038518 0.047746 -0.097145 -0.025404 0.011637 252 | Industry 0.034139 0.016642 0.066210 -0.026955 -0.100935 253 | Force 0.024578 0.011978 0.081486 0.015329 -0.085079 254 | Amazon -0.033822 -0.070641 -0.010357 -0.076198 -0.061705 255 | Steve -0.063396 0.038372 0.093942 0.050317 0.087316 256 | ratings 0.020546 0.011316 -0.007560 0.009746 0.032018 257 | against 0.025058 0.055267 -0.039412 -0.094593 -0.070990 258 | Lanka 0.041185 -0.027810 0.024655 -0.013064 -0.115853 259 | launch -0.037175 -0.102275 -0.100532 0.012921 -0.022030 260 | 2,000-year-old 0.046385 0.063140 0.087843 0.067586 0.048914 261 | BP -0.059495 0.005213 -0.070874 0.065190 -0.057842 262 | don't 0.078367 0.047200 0.014534 0.051330 -0.065947 263 | like 0.036951 0.092134 -0.063891 -0.083683 -0.029760 264 | seeing 0.031066 -0.075102 0.062084 -0.009460 0.052150 265 | subprime -0.048924 -0.077166 0.005043 -0.090475 -0.046195 266 | mess 0.006574 0.060886 0.057450 -0.007225 0.019127 267 | China's -0.084895 -0.035539 -0.031680 0.087289 0.066324 268 | rise -0.047656 -0.095789 0.054329 0.050522 -0.035842 269 | Do 0.077198 0.070770 0.008162 0.004321 -0.076663 270 | At-Home 0.037375 0.069432 -0.031070 0.052389 0.036219 271 | Genetic -0.080927 -0.042936 0.027409 0.021455 -0.103668 272 | Tests -0.077170 -0.005235 0.095322 0.026266 0.034685 273 | Tell -0.026249 -0.096173 -0.014529 -0.091780 0.006807 274 | Much 0.015773 -0.075772 -0.066695 -0.000742 -0.058099 275 | Explain 0.018642 0.099930 0.013863 -0.002731 -0.013474 276 | Little? 0.033840 0.096749 0.076216 -0.036905 0.017319 277 | nuclear -0.041679 -0.027602 0.001668 -0.042338 0.000143 278 | Rodman 0.054680 -0.083174 0.071411 -0.095176 -0.088831 279 | Hall -0.042222 -0.026905 -0.053527 0.092567 0.045687 280 | take -0.035015 0.052943 -0.065528 0.019116 -0.005089 281 | starting -0.062766 -0.058856 -0.019081 0.003175 -0.038968 282 | Monday 0.096336 -0.077811 -0.050113 -0.030194 -0.037521 283 | Web -0.099617 -0.031268 0.098951 0.090492 -0.002650 284 | tour 0.062779 0.048511 -0.039502 -0.082464 -0.031141 285 | 18 -0.033039 -0.044434 0.041108 -0.003591 0.005485 286 | 30 -0.061695 -0.093213 0.092942 0.042237 -0.015533 287 | Years -0.013452 -0.033947 0.058893 0.031515 -0.075649 288 | More 0.091375 -0.090189 -0.098670 0.000067 0.033185 289 | Stars 0.080724 -0.022506 0.009877 -0.075107 -0.030280 290 | bill 0.051181 -0.029130 0.065625 -0.049211 0.005473 291 | David 0.040067 0.081792 0.093821 -0.027233 -0.007993 292 | American -0.077221 0.087672 0.042720 0.094603 0.023021 293 | bankruptcy 0.038890 -0.065265 -0.008512 -0.096114 0.068104 294 | more -0.027894 0.064466 -0.076510 -0.017718 0.050041 295 | Barbour's 0.055384 -0.086077 0.010374 -0.014417 -0.018749 296 | wife 0.086142 -0.029016 -0.091654 -0.077516 -0.049787 297 | shortage 0.082863 0.045484 0.096978 0.089474 0.003727 298 | Against 0.070946 -0.009012 0.089057 -0.015568 -0.025511 299 | can't -0.060570 -0.024239 0.061971 0.073989 0.016993 300 | Has 0.053955 0.006992 0.000330 -0.010657 0.021571 301 | Porn 0.009567 -0.032653 -0.036867 -0.060022 -0.073724 302 | Memphis -0.040811 0.036235 -0.007526 -0.002781 0.023857 303 | fund 0.076630 -0.091641 -0.006340 -0.083858 -0.032907 304 | England 0.081384 -0.032161 0.048557 -0.052401 0.033991 305 | Ben 0.074909 0.062890 0.025732 0.098861 0.071989 306 | Measure -0.000371 -0.087351 -0.085968 0.016589 0.065634 307 | banned 0.014859 -0.031390 0.025265 0.010576 -0.039865 308 | Vogue -0.067707 -0.063743 -0.072165 -0.043501 -0.027337 309 | (PHOTOS) -0.047722 -0.068849 0.085191 0.019642 0.069182 310 | sex 0.073683 0.037821 0.071860 0.001581 -0.102657 311 | Some -0.001520 0.087649 -0.039659 0.073659 0.071368 312 | Gadhafi's -0.006651 -0.031600 -0.036649 -0.074324 -0.063208 313 | gas 0.070119 0.063641 -0.078364 0.072523 -0.030200 314 | Europe -0.001370 -0.046323 -0.044482 0.061111 -0.037268 315 | become -0.046740 -0.083277 0.026729 -0.058528 0.004051 316 | prosecutor -0.061503 -0.082615 0.058247 -0.034333 0.072330 317 | took 0.036836 -0.036782 -0.047712 0.004799 0.055155 318 | Bruno 0.042068 -0.008737 -0.010209 0.079734 0.055368 319 | Paris -0.077080 -0.075400 0.055584 0.063416 0.000794 320 | Hilton 0.016439 0.039688 0.066901 0.032309 -0.079481 321 | being -0.006048 0.072382 -0.037618 -0.062927 -0.032410 322 | crack 0.061881 -0.097576 0.048112 -0.067384 -0.109362 323 | shot 0.040223 0.040728 0.022043 0.031576 0.063635 324 | creates -0.065553 -0.089572 0.054659 -0.058558 -0.054924 325 | row -0.058193 0.083399 0.086108 0.024466 0.066531 326 | Pour -0.000064 0.062580 -0.066192 -0.080454 -0.037281 327 | Syrup -0.076867 -0.019395 0.061557 -0.035194 -0.002411 328 | Good -0.072902 0.029125 0.083348 0.013279 -0.026927 329 | French -0.033633 -0.057443 -0.094047 -0.006673 0.010335 330 | Movie -0.038122 0.053227 0.022557 -0.063798 -0.088175 331 | Acer 0.084037 -0.050519 -0.009328 -0.093822 -0.087184 332 | pledges 0.080831 0.022431 -0.014207 0.045784 0.072409 333 | efforts 0.024739 -0.004005 -0.080888 0.038209 0.045991 334 | rebound 0.066061 -0.039696 0.090025 -0.074528 -0.009859 335 | amid 0.062033 0.092647 -0.063561 0.093878 0.013954 336 | slowing -0.002172 -0.042937 0.042065 0.034272 0.020055 337 | we -0.038053 -0.037317 -0.108861 0.035227 -0.059072 338 | workout -0.024280 -0.044868 0.054928 -0.001607 0.065578 339 | video 0.031738 -0.011464 0.068637 0.086022 0.091204 340 | Republicans 0.020750 0.002574 0.089773 -0.035716 -0.065015 341 | Strategic -0.051626 -0.108762 0.023474 -0.104538 0.063290 342 | theaters -0.070739 -0.020901 -0.069410 0.062961 0.066252 343 | this -0.085300 0.073061 -0.045169 -0.012744 -0.026132 344 | summer -0.080354 0.058317 0.045267 0.067786 -0.065499 345 | Panel 0.024696 0.032458 -0.016302 0.025038 -0.072312 346 | Action -0.032602 -0.101313 0.084591 0.036335 -0.098880 347 | Dyes -0.049845 0.079932 0.010969 -0.057410 -0.072586 348 | Used -0.033533 0.098184 0.041374 -0.068156 0.090522 349 | Foods -0.083505 0.001898 -0.013502 0.023970 0.070058 350 | release -0.017417 -0.086499 -0.020228 -0.088468 0.046345 351 | Assad 0.082600 0.046513 0.010918 0.067422 -0.072760 352 | appoints 0.013641 -0.026655 0.052344 0.059322 -0.056301 353 | Adel -0.056017 -0.083804 -0.072701 -0.052149 0.039678 354 | Syria 0.029584 -0.013813 -0.070513 -0.003177 -0.100307 355 | 8.8% 0.046991 -0.013897 0.006023 -0.098669 -0.007566 356 | Facebook's -0.003803 -0.091372 -0.080819 0.073248 0.021797 357 | Fired -0.069991 -0.041985 -0.030685 0.021384 -0.027079 358 | Manager: 0.073664 0.071446 -0.028494 -0.032707 -0.102872 359 | I 0.023852 0.039257 -0.094186 -0.011968 -0.099951 360 | Did 0.003543 0.014328 -0.008246 -0.071800 -0.080591 361 | Nothing -0.060675 0.033556 0.099752 0.008899 -0.077956 362 | Wrong 0.009861 0.084598 -0.006931 -0.009201 -0.087735 363 | Jersey -0.016363 -0.063263 0.076385 0.073148 0.090067 364 | District 0.065833 -0.073627 -0.085481 -0.053996 0.068554 365 | Suspends -0.023085 -0.055591 -0.017968 0.015661 0.005153 366 | Drugmaker -0.056178 -0.078063 -0.096836 0.059169 -0.014937 367 | scales -0.036725 -0.019854 0.052399 0.073373 -0.023095 368 | sharp 0.041571 0.012112 -0.021253 -0.049300 0.047939 369 | hike 0.026263 -0.014201 0.065530 -0.037534 -0.031138 370 | price 0.092183 0.067100 0.018813 0.058811 -0.030582 371 | pregnancy -0.032810 -0.079002 -0.025962 0.023174 -0.097854 372 | Balenciaga 0.077522 -0.005804 0.001399 -0.021471 -0.033523 373 | Museum -0.072785 -0.044762 0.054322 0.032020 -0.044341 374 | Mobile -0.018581 0.092853 0.027752 -0.090428 -0.024287 375 | murder -0.104826 -0.049853 -0.058229 -0.052468 0.017307 376 | Chloe 0.053329 0.075994 -0.063039 -0.045473 -0.026598 377 | Restaurant 0.053731 0.098625 0.018645 0.049995 -0.011919 378 | hit -0.040939 -0.054750 0.064758 -0.003920 0.063095 379 | Radiation -0.078307 0.088893 0.082129 -0.008334 -0.017628 380 | Suns 0.069200 0.004105 -0.090414 -0.027485 -0.044723 381 | Clippers 0.042824 -0.042735 -0.058418 0.008921 0.013486 382 | about -0.090168 0.039008 -0.004716 -0.041139 0.018712 383 | Cuts -0.011554 0.077235 0.058553 -0.067857 0.004181 384 | As 0.095441 -0.017824 -0.025167 0.056695 -0.033492 385 | Gmail 0.040021 -0.070297 0.026532 0.057654 0.023834 386 | Fools' -0.060501 -0.034446 -0.034746 -0.061897 -0.096493 387 | (video) -0.006167 -0.017377 -0.009051 0.027317 0.080837 388 | Haley 0.072589 -0.009174 0.096683 -0.016534 -0.099535 389 | Wife -0.082059 -0.050752 0.045905 0.011067 -0.092409 390 | Be -0.082056 -0.069335 0.004672 -0.080097 0.093862 391 | Yankees -0.024187 -0.096412 -0.011862 0.008462 -0.082263 392 | hand -0.043062 0.081349 0.043037 -0.079833 -0.065078 393 | signals 0.030234 -0.081606 0.096660 0.009746 -0.092460 394 | radioactive 0.059267 -0.056184 -0.031595 -0.014759 -0.088202 395 | cases -0.077530 -0.095222 0.056433 -0.039409 -0.097991 396 | disease -0.093447 -0.039926 -0.021165 -0.076135 0.005302 397 | rally -0.090053 -0.025109 -0.022426 -0.047572 0.019003 398 | Verizon -0.012770 -0.006341 0.015459 -0.057332 -0.008067 399 | Android 0.051880 0.075686 0.058888 -0.020511 -0.032003 400 | play 0.032451 0.050702 -0.064758 0.007622 -0.118251 401 | ""The -0.016835 -0.034288 -0.062319 -0.091009 0.077159 402 | Qwest -0.077948 -0.108854 -0.070619 -0.107513 -0.042887 403 | wants 0.016991 -0.043929 0.045143 0.091897 -0.025332 404 | no 0.014717 0.030508 0.097699 0.047255 -0.055459 405 | From 0.002729 -0.100433 0.063815 -0.084277 0.053684 406 | worst 0.019285 0.018801 -0.102802 -0.049150 -0.093028 407 | their -0.030623 0.013565 -0.061766 -0.098758 0.032490 408 | fight -0.044948 -0.104468 -0.015850 0.062743 -0.057625 409 | rival 0.077745 0.066265 0.065303 0.047236 0.080327 410 | guard -0.009363 0.002915 -0.082499 -0.006844 -0.092768 411 | tried -0.068064 -0.000913 -0.010581 -0.060929 0.006833 412 | AL 0.020734 -0.081974 -0.098053 -0.047244 -0.012014 413 | line 0.002747 -0.052543 0.020431 0.056523 0.036660 414 | Doesn't 0.019537 -0.015625 -0.049202 -0.026817 -0.077118 415 | charges 0.016591 0.003048 -0.018443 -0.090702 0.059622 416 | $40b -0.055395 -0.023195 -0.055572 0.014030 -0.038079 417 | accused 0.064077 0.064945 0.031209 0.023922 -0.050661 418 | Lady -0.089515 -0.105360 0.078566 0.041342 0.045543 419 | Gaga -0.082015 0.075003 -0.052647 0.098895 -0.029518 420 | years 0.082632 -0.084762 -0.023563 -0.062534 -0.037463 421 | True -0.065408 -0.066530 -0.012373 0.062161 0.053715 422 | May -0.004397 0.075623 0.031156 -0.001241 -0.023807 423 | UPDATE -0.056979 -0.101828 -0.078358 -0.048739 0.001377 424 | Air 0.064657 -0.048558 -0.016231 -0.024509 0.057747 425 | goal -0.045373 -0.074172 -0.099436 0.094891 0.004960 426 | EU -0.033468 -0.091628 0.076237 0.013310 0.005131 427 | 4-3 0.075658 0.018563 0.067005 0.001295 -0.016645 428 | calls -0.032286 -0.006528 -0.022556 -0.061442 -0.081861 429 | Group 0.058771 0.009616 -0.002318 0.058422 -0.095962 430 | police 0.031956 0.001267 0.031985 0.082747 0.064153 431 | Fools -0.094193 -0.031122 0.031277 0.068335 -0.057378 432 | Day -0.049770 0.092062 0.048911 0.083465 -0.091829 433 | Blames 0.052501 0.008584 0.072900 0.098543 -0.049113 434 | Suckiness 0.023387 0.028023 -0.022731 0.064029 0.021205 435 | YouTube 0.066526 0.020490 0.046595 0.031154 -0.049961 436 | Doctrine -0.038131 0.014325 0.043389 -0.084724 -0.072129 437 | suspect -0.055757 -0.047118 -0.018964 -0.084359 0.042397 438 | Department -0.061681 -0.059081 0.089653 0.034565 0.089613 439 | speech 0.022711 0.008608 -0.073922 0.078674 -0.022478 440 | better -0.079709 -0.083239 -0.055508 0.064437 0.012164 441 | say 0.032574 0.052229 -0.088078 -0.070893 -0.088977 442 | Federal 0.053730 -0.081428 0.023726 -0.072495 -0.097445 443 | NH 0.041149 -0.059411 -0.051659 -0.082396 -0.092961 444 | suit -0.061092 -0.099944 0.029099 0.080374 0.047644 445 | child -0.023121 0.001086 0.079666 0.004503 -0.073266 446 | return -0.038912 0.046430 0.033949 -0.070347 0.079155 447 | Reisch -0.012770 -0.015583 0.080848 0.000881 -0.077483 448 | commander -0.058950 -0.034642 0.003863 -0.076872 -0.020286 449 | National -0.022784 0.054525 0.020459 0.076256 0.042152 450 | calling 0.003742 -0.039916 -0.072257 0.002140 0.000686 451 | 53 -0.073914 -0.015273 0.089015 -0.038485 0.054831 452 | live 0.011080 0.087891 -0.091204 0.037733 0.000657 453 | Israeli -0.096453 -0.059291 0.065005 -0.087539 -0.107390 454 | Maple -0.028087 -0.004891 -0.082222 0.021989 -0.034709 455 | syrup -0.026439 -0.066487 -0.009231 -0.092640 -0.045828 456 | Walk -0.039296 -0.026386 0.070386 -0.092508 0.012121 457 | drop 0.046483 0.016152 -0.095285 -0.057233 0.090472 458 | stocks -0.052746 0.058473 -0.039878 0.011490 0.042829 459 | arrest 0.021744 0.063246 -0.055959 -0.052793 0.028240 460 | site 0.008151 -0.073154 0.003881 -0.091480 -0.090456 461 | Continue 0.083675 -0.033208 0.098985 0.000511 0.027362 462 | GOP 0.037552 -0.054740 0.085011 -0.079102 0.092839 463 | House 0.061520 -0.073693 -0.041205 0.093376 -0.074124 464 | budget 0.038171 0.076286 0.071256 -0.003848 0.083716 465 | Azarenka -0.038711 0.071585 -0.040833 0.078462 0.009489 466 | Sharapova 0.028670 0.041492 -0.100556 0.013183 -0.032362 467 | title -0.098112 -0.009789 0.014779 0.012139 -0.087423 468 | Plan 0.007091 -0.069344 0.048981 -0.095751 -0.033076 469 | day 0.064522 0.074740 -0.117004 -0.053070 -0.052066 470 | Agreement 0.022797 -0.104082 0.036421 -0.005441 -0.009297 471 | Boston 0.027014 -0.047775 0.092555 0.067302 0.089194 472 | GM 0.000850 0.036017 -0.095369 -0.012521 -0.018617 473 | leader -0.027748 -0.088230 -0.044562 0.009137 -0.023581 474 | Your -0.038697 -0.018555 -0.069065 0.071519 -0.128541 475 | Says 0.038414 0.040351 0.037326 -0.026850 -0.052711 476 | likely 0.061841 0.032676 -0.046296 -0.072609 0.053154 477 | clean 0.032220 -0.053705 0.085225 -0.082420 -0.113364 478 | Crimesider -0.075017 -0.068533 0.022016 0.079225 -0.022036 479 | leaves -0.093421 -0.083284 0.073755 -0.038169 -0.039480 480 | Vegetarians -0.089549 -0.096912 0.008071 0.093626 0.040888 481 | lower 0.035516 -0.001224 0.057149 -0.011226 0.094373 482 | diabetes, -0.064630 0.009844 0.067469 0.025949 -0.073103 483 | heart 0.023792 0.094083 0.018773 0.034486 -0.053256 484 | risk 0.038551 0.042760 0.020766 -0.000284 0.091792 485 | Portugal 0.013651 -0.092166 0.084484 -0.052658 -0.000169 486 | paper -0.103154 -0.071332 -0.096641 0.040018 0.000829 487 | steps -0.100451 -0.094735 -0.020842 0.001442 -0.018839 488 | Vegas -0.049577 0.016958 0.078409 -0.015315 -0.018256 489 | medicine -0.047686 0.097384 -0.022059 -0.042484 -0.030002 490 | 4: 0.027722 0.089376 0.040751 0.061716 -0.052209 491 | milk 0.067008 0.060176 0.001433 0.002630 0.010566 492 | 5 -0.065755 -0.038075 -0.028298 0.053731 0.092900 493 | students -0.036154 -0.061443 0.028581 0.083445 -0.049184 494 | tests 0.031438 0.084198 0.059662 -0.006251 0.034574 495 | Metro 0.074356 -0.060294 -0.056754 -0.049316 0.075053 496 | kills 0.028782 -0.082406 -0.101969 0.074069 0.072632 497 | policeman -0.040453 0.030732 0.068894 -0.081293 -0.081122 498 | Weekend 0.075169 0.031942 0.062475 0.066278 -0.103910 499 | Check -0.067955 -0.057218 0.040089 0.075895 -0.066308 500 | cops -0.073484 0.032178 0.071676 -0.014337 0.014170 501 | spending -0.002148 0.040916 0.006988 -0.034967 0.003174 502 | lifts 0.062715 0.007380 0.041470 0.053456 -0.052141 503 | Review 0.030844 -0.019783 0.085589 0.081793 -0.084353 504 | First 0.076985 0.060106 0.072391 0.082888 -0.094091 505 | faces -0.063436 -0.089180 0.096005 0.043458 -0.032449 506 | investigation -0.023230 -0.062086 0.038056 -0.032183 -0.014503 507 | stars 0.034231 0.002805 -0.066609 -0.083631 0.065091 508 | goes 0.069676 -0.053223 0.067988 0.050155 -0.066361 509 | Dolce 0.008000 -0.034152 -0.040743 -0.048000 0.067388 510 | Gabbana 0.047224 0.027926 -0.086515 -0.020163 -0.063108 511 | trial 0.082784 -0.041675 -0.014319 0.061378 -0.098381 512 | £1bn -0.055137 0.071758 -0.076348 0.044573 0.070008 513 | thrown 0.082030 -0.073645 0.073235 -0.092737 -0.042156 514 | Palace 0.008731 -0.040806 -0.088909 -0.007391 -0.046195 515 | operation 0.086625 0.019186 0.048678 -0.048315 0.070642 516 | Report -0.002686 -0.054948 0.061577 -0.045786 0.065024 517 | Judge 0.005807 -0.014068 -0.080883 -0.069780 0.057038 518 | sentences 0.083100 0.028634 -0.066362 0.051781 -0.037156 519 | hanging 0.057694 0.079927 0.058638 0.050851 -0.030276 520 | app 0.053987 -0.037022 -0.015153 -0.015883 -0.095455 521 | now 0.012078 0.028115 0.084099 0.024366 0.026448 522 | Phillies 0.027083 0.042404 -0.035867 -0.042453 -0.106061 523 | Reveals 0.099060 0.061901 0.083382 -0.041567 -0.053194 524 | Beauty 0.085133 0.080564 0.026904 0.018391 -0.100951 525 | Shows -0.035092 0.048246 -0.055850 0.069620 0.065881 526 | girls 0.073284 -0.103969 -0.073663 0.008337 -0.087134 527 | Rep. 0.014283 -0.090663 0.030540 0.001178 0.087070 528 | Mexico 0.024123 -0.021309 -0.086385 0.091538 -0.108897 529 | Officer 0.051865 0.043620 -0.099749 -0.014534 -0.060285 530 | Ericsson 0.050543 -0.046440 -0.029504 -0.033244 -0.083629 531 | stake 0.055291 -0.086046 -0.111099 -0.023938 0.004001 532 | Buy -0.053133 -0.024094 -0.000254 0.042226 0.019044 533 | Police 0.017132 0.069557 -0.084414 -0.067763 -0.026456 534 | Digits -0.005887 0.019392 0.092883 -0.026655 0.057415 535 | Many -0.067493 -0.041039 -0.005580 0.039476 0.057528 536 | multiple 0.056668 -0.105148 -0.077719 0.086209 0.064026 537 | fathers 0.073060 0.053332 0.028536 0.034092 -0.064086 538 | Fast-food 0.074510 0.072984 0.036359 0.059806 0.040355 539 | + 0.030070 -0.054621 -0.102584 0.039753 -0.092029 540 | coffee -0.020754 -0.080020 -0.114295 0.079407 -0.111022 541 | = 0.021163 -0.000633 0.052076 -0.061799 -0.123508 542 | soaring 0.019380 0.034404 0.009979 0.020257 -0.035152 543 | blood 0.024726 -0.105626 -0.087055 0.027224 -0.137162 544 | sugar 0.001577 0.034562 0.027568 -0.058038 -0.066727 545 | leaks -0.070347 0.098686 0.024617 -0.082428 0.028841 546 | ""Like"" 0.015777 0.045337 0.086555 -0.098680 0.091193 547 | business -0.093425 -0.087033 0.022001 0.027380 -0.038832 548 | Orioles 0.040054 -0.001538 0.053496 -0.039706 0.007837 549 | legend 0.003490 -0.005848 -0.063794 0.068968 -0.016541 550 | Robinson 0.066412 -0.098514 -0.007681 -0.013601 0.050840 551 | recuperating 0.002937 0.096106 -0.039596 0.060328 0.056742 552 | High-end -0.077640 -0.058683 -0.065244 0.004294 -0.062184 553 | medical -0.002302 0.055913 0.068732 0.053248 0.063814 554 | option 0.001833 0.057729 -0.056361 -0.081197 -0.096593 555 | prompts -0.090619 -0.066141 0.067768 -0.070116 -0.049453 556 | worries 0.062704 0.004175 -0.087104 0.022755 0.029605 557 | Licenses -0.023542 0.064026 -0.022522 0.033583 -0.063371 558 | Cloud 0.027109 -0.048159 0.002262 0.034679 -0.052449 559 | Player -0.010239 0.096834 -0.065053 0.020459 0.035021 560 | — -0.071730 0.007677 -0.022518 -0.080731 -0.023567 561 | or -0.018150 0.027307 0.086823 -0.074262 0.001254 562 | Else -0.051066 -0.071451 -0.064975 -0.091292 -0.034560 563 | Retiree -0.019143 0.097571 0.091487 0.072385 -0.073562 564 | cost -0.096856 0.015719 -0.081613 -0.027812 -0.091730 565 | estimate 0.043015 0.047713 -0.071930 -0.017955 -0.077524 566 | falls, 0.014310 -0.037171 -0.009250 0.017105 -0.099305 567 | change 0.062253 0.016021 -0.053661 -0.036337 0.084769 568 | Carell 0.015842 0.007683 0.061216 -0.076138 -0.029737 569 | ""Seeking -0.011079 -0.056665 -0.065288 -0.015895 0.058440 570 | Friend"" -0.004791 0.080237 -0.039727 -0.079023 -0.067818 571 | Keira 0.098479 0.045485 0.064538 -0.077785 0.000549 572 | Knightley -0.088602 0.054790 0.094865 0.035102 0.060802 573 | NEW -0.085327 0.014435 0.070507 0.041385 -0.002959 574 | "The 0.027600 0.063279 0.097527 0.069387 0.011963 575 | Hangover 0.019026 0.010110 0.042201 0.099146 -0.062068 576 | Part -0.056126 0.013589 -0.004349 0.058859 -0.074866 577 | 2" -0.056751 0.009205 0.084492 0.071469 0.002448 578 | trailer 0.025243 0.023587 -0.035501 0.027010 -0.001401 579 | My 0.084329 -0.087868 -0.051224 -0.080843 0.033694 580 | favourite 0.047283 -0.097062 -0.080183 0.040058 0.087269 581 | particle: -0.007887 0.024473 0.055891 -0.086610 -0.049710 582 | neutrino -0.012670 -0.091950 0.009054 0.079402 -0.009458 583 | player 0.069783 -0.055477 -0.045455 -0.055647 -0.076155 584 | Cricket 0.090332 -0.019365 0.024863 0.066254 -0.085852 585 | Christina 0.089918 0.066150 0.052606 0.098308 -0.005993 586 | Hendricks' 0.079785 -0.021403 -0.077485 -0.025959 -0.024423 587 | breasts 0.053642 0.085291 0.038022 -0.010759 0.044131 588 | Vivienne -0.032986 0.029984 -0.070109 0.031766 0.031204 589 | Westwood 0.007388 0.030024 0.024643 -0.091120 0.023796 590 | USGS -0.056316 0.082508 0.072524 -0.090130 -0.021340 591 | Finds -0.004675 -0.062218 0.007509 0.050900 -0.092834 592 | Coral 0.089779 -0.042423 0.011824 -0.065526 -0.075224 593 | Near -0.007028 -0.089320 -0.091810 -0.081520 0.044370 594 | Oil 0.076749 -0.064574 -0.001540 0.022824 -0.064963 595 | Spill: 0.093283 0.073151 0.029962 0.007331 -0.067102 596 | Was -0.063257 0.058076 -0.063810 0.000851 0.068270 597 | Damaged? -0.008300 -0.001941 0.089924 -0.059320 -0.062732 598 | SC 0.055897 -0.011291 0.076688 0.070773 -0.080975 599 | 13 0.053401 -0.034614 0.097259 0.025494 -0.060518 600 | dogs 0.081207 0.040821 -0.075626 0.025245 0.095846 601 | wild -0.048020 -0.031346 -0.062699 0.092519 -0.049303 602 | home 0.053111 -0.089185 0.061714 0.048661 0.041170 603 | Hawks 0.092784 -0.004946 -0.080961 -0.039021 0.057813 604 | Celtics -0.033293 0.083003 -0.092924 0.033895 0.048615 605 | 4th -0.019101 0.073410 0.058330 0.018026 -0.046520 606 | straight 0.012486 -0.095022 0.060724 -0.039034 0.096865 607 | Marsden's 0.011427 -0.003820 0.002727 -0.006950 0.075775 608 | dad 0.024758 -0.018284 0.042571 -0.010690 0.057807 609 | screen -0.061521 0.028007 -0.075336 0.088103 -0.003810 610 | soon: -0.004281 0.008206 0.051504 0.012279 0.010204 611 | CPI -0.027563 -0.014977 0.062146 -0.070379 0.045830 612 | exceed 0.031074 -0.047441 0.080979 -0.098766 0.048049 613 | 5%: 0.083573 0.015516 -0.038941 -0.007776 -0.098930 614 | economists -0.057728 0.028543 -0.083148 -0.026971 0.071920 615 | Retail 0.021604 0.032941 0.005648 0.067833 0.062622 616 | banks 0.073087 -0.018503 -0.086643 -0.009955 -0.004431 617 | Britain -0.077612 -0.092549 -0.061251 0.047032 -0.052007 618 | stung -0.025393 0.018455 -0.056276 -0.043691 0.007531 619 | eBay -0.059998 -0.088593 -0.056567 -0.078154 -0.073009 620 | releases 0.029728 0.037010 0.063123 0.055466 0.072596 621 | J-Power 0.018924 -0.000040 -0.083850 -0.033435 0.088217 622 | halts 0.035440 -0.051550 0.050643 0.072240 -0.062498 623 | construction 0.019793 0.094814 -0.024196 0.090776 0.098229 624 | work -0.087726 0.010337 -0.075862 -0.014302 0.034263 625 | Ohma 0.012205 -0.016941 -0.051040 -0.003457 -0.026603 626 | possess 0.012985 0.067115 -0.094820 -0.002092 -0.065873 627 | Premier -0.051194 -0.032011 -0.039842 0.086026 0.004459 628 | League: -0.096098 0.080809 -0.061362 0.037222 0.011978 629 | Carlo -0.031301 -0.044179 0.081486 -0.031360 -0.045976 630 | Ancelotti -0.098106 0.033150 -0.042926 0.011286 0.045341 631 | Tili's 0.084776 0.021608 -0.037835 -0.037203 0.008859 632 | SeaWorld 0.020616 0.024597 -0.081926 -0.038735 0.006620 633 | crowds: 0.054164 0.043871 -0.081688 -0.040173 -0.013377 634 | Tips 0.077978 -0.083894 -0.018967 0.023706 -0.032102 635 | orca 0.087000 0.007121 -0.074766 0.094719 -0.042517 636 | show -0.065114 0.016237 0.076558 0.077429 0.041766 637 | Malicious -0.010455 -0.079733 -0.057942 0.083429 -0.032786 638 | pages -0.002391 -0.097412 0.028158 -0.012505 -0.065552 639 | Leadership 0.059264 0.097698 0.019402 -0.034495 0.003095 640 | changes 0.028079 0.047698 -0.033498 0.071483 0.064731 641 | made 0.092940 0.010444 -0.019626 0.098714 0.046399 642 | "Glee's" -0.068024 -0.017304 -0.018939 -0.004917 -0.050819 643 | Mr. -0.040266 0.100258 -0.053212 0.072688 0.013304 644 | Schue 0.077304 -0.073457 0.040683 -0.009182 0.047674 645 | (Matthew -0.026463 -0.088096 -0.001843 0.069264 -0.078899 646 | Morrison) 0.080801 0.032785 0.052181 0.041140 -0.022942 647 | kick 0.050794 0.007158 0.053279 0.080964 -0.055479 648 | Minneapolis -0.004238 -0.082164 0.036368 0.015169 -0.029621 649 | June -0.045106 -0.048846 0.097022 0.096221 0.085058 650 | CDC: 0.090155 0.017599 0.096666 -0.097299 -0.026348 651 | Drop -0.075188 0.054759 0.000514 -0.029263 -0.055594 652 | Birth 0.073113 0.051132 -0.086967 -0.037786 0.074987 653 | Biggest 0.025288 0.066370 0.047218 0.073880 0.012191 654 | RockMelt 0.044067 0.049260 0.075660 -0.039305 -0.055917 655 | Rethinks 0.085765 -0.038967 -0.062684 0.047134 0.010538 656 | Browser -0.062442 -0.051363 0.095312 -0.030774 0.086188 657 | Social -0.043119 0.016234 0.081346 0.058577 -0.021491 658 | Inside -0.066021 0.028434 -0.002684 0.032260 -0.084813 659 | CBS -0.058575 -0.070582 -0.064860 -0.094015 -0.000440 660 | Conscripts 0.025342 0.071537 0.063189 -0.073547 0.033908 661 | Twittering -0.064049 -0.038815 0.054513 -0.056183 0.060701 662 | #CBSTweetWeek -0.074451 0.050987 -0.001921 -0.032420 -0.090458 663 | Alaska -0.054055 -0.004333 0.081433 -0.012239 -0.058296 664 | draws 0.090224 -0.095820 0.073530 0.002463 -0.050790 665 | early -0.097416 0.065382 0.080147 0.090960 -0.020812 666 | fire -0.067298 -0.097711 -0.091850 -0.018192 -0.000333 667 | Hermes -0.075940 0.020459 0.051316 -0.019950 0.094092 668 | Sell -0.007854 0.086355 -0.076604 0.074608 0.046257 669 | Stake 0.029434 -0.095066 0.037473 0.068982 0.035884 670 | Jean-Paul 0.083439 0.018502 -0.028827 0.045747 -0.002500 671 | Gaultier -0.079612 0.034765 -0.009849 0.046031 0.012727 672 | Company 0.022677 0.015393 -0.083391 0.067636 -0.016893 673 | Edelstein -0.085070 -0.099292 0.052009 -0.059779 0.046738 674 | Lincoln -0.021054 0.033589 0.038495 0.018687 -0.074252 675 | Lawyer -0.000885 0.041466 0.058341 -0.041969 -0.069383 676 | , -0.012516 -0.088354 -0.050218 -0.091740 0.070731 677 | Rand -0.041240 -0.033062 -0.008264 -0.042643 0.005415 678 | Paul’s -0.050668 -0.052498 0.012319 -0.062734 -0.022725 679 | Toilet 0.082020 -0.071470 0.088597 0.022262 -0.056722 680 | Issues 0.013292 0.017732 -0.012377 -0.048485 -0.007948 681 | Ka'aihue -0.062176 0.065648 0.009100 -0.063458 0.052524 682 | Homer -0.093619 0.083167 -0.011645 0.091110 -0.011676 683 | Powers -0.014213 -0.032172 0.000559 0.012372 -0.032742 684 | Kansas 0.062389 0.032189 -0.043788 -0.090009 -0.034832 685 | Past 0.051243 0.033077 -0.036927 -0.050256 0.033403 686 | Angels -0.028323 0.028411 0.037909 -0.042263 0.078034 687 | 2-1 0.099540 0.081170 -0.047116 -0.021915 -0.046791 688 | Apparel -0.093981 -0.051843 -0.056143 -0.092891 -0.088071 689 | warns 0.004747 -0.043348 -0.064144 0.089059 0.094868 690 | file -0.059262 -0.088979 -0.012727 -0.016375 0.054589 691 | Ristretto 0.032112 -0.079889 -0.093513 -0.066941 0.047658 692 | Modernist 0.040415 0.017888 0.089495 -0.068763 0.057749 693 | Coffee -0.034536 0.060569 0.086017 0.049766 0.065090 694 | Egypt 0.077886 0.015945 0.087482 0.019425 0.090458 695 | introduces 0.029083 0.022539 0.063064 0.033145 -0.091406 696 | provisional -0.031579 0.026270 0.064956 0.039543 -0.054841 697 | constitution -0.089810 0.018408 0.095389 0.100248 -0.008927 698 | Depp 0.053442 -0.057070 0.013573 -0.088985 -0.096140 699 | Pirates 0.060762 0.012815 0.086832 0.012458 0.092690 700 | ""horrified"" 0.039586 0.023816 0.032182 0.067274 -0.072922 701 | presidential 0.036060 0.061074 0.084532 0.055194 0.000792 702 | prospects 0.080948 -0.053876 -0.022193 -0.043139 0.004411 703 | Isaac -0.036804 -0.061073 0.049085 0.033932 0.010641 704 | Mizrahi 0.050590 0.060938 0.036483 0.094261 -0.050461 705 | bridal -0.040772 0.080375 -0.086347 0.074351 0.031268 706 | dream 0.063558 0.080212 0.006143 0.090567 0.043685 707 | dresses -0.062032 0.072331 0.086370 0.100645 -0.078981 708 | exclusively 0.018245 -0.051467 0.005230 -0.069948 -0.061983 709 | Aisle -0.089857 -0.072189 -0.081860 -0.074116 -0.077114 710 | Glencore -0.080468 -0.098546 0.072946 0.019152 -0.034556 711 | HK -0.068421 0.065574 0.091492 -0.020354 0.032445 712 | nod -0.051671 0.094329 0.027379 -0.038672 0.077061 713 | planned -0.072881 0.084789 -0.038824 0.008941 0.053844 714 | $10 0.017920 -0.067214 -0.008462 0.090932 -0.099002 715 | billion -0.089196 0.026357 0.081769 -0.030222 -0.055379 716 | IPO 0.077192 0.017429 0.022824 0.057455 -0.010354 717 | Diane 0.088312 0.061130 -0.072274 -0.051985 0.081969 718 | Gives -0.088826 0.006740 -0.065730 0.045719 -0.000768 719 | Photo 0.084636 0.065417 0.066631 0.064175 0.004270 720 | atlas: -0.005677 -0.082174 -0.036940 -0.063289 0.079008 721 | earthquake -0.049152 0.017290 0.021798 -0.071065 -0.046524 722 | Midwife 0.016771 0.086108 0.074213 0.097154 -0.048121 723 | costs -0.066358 0.037141 -0.047686 -0.031839 0.036617 724 | lives, 0.017013 -0.022236 0.006914 0.079089 -0.081236 725 | services -0.005353 0.002007 0.059783 0.015091 -0.056471 726 | disrupted 0.062766 -0.041464 -0.085040 0.101080 0.044891 727 | collision 0.085538 -0.095776 -0.026907 0.017391 -0.035853 728 | Express 0.005158 -0.079123 0.055037 0.014324 0.034538 729 | Opens 0.016007 0.001087 0.028300 -0.088032 0.049152 730 | Campaign 0.045933 0.017113 -0.040413 -0.070463 -0.042503 731 | Yoga -0.054828 0.043982 -0.013909 -0.019157 0.064065 732 | halves -0.047641 -0.037066 0.011223 0.067389 -0.048029 733 | irregular-heartbeat 0.025935 0.063768 0.036084 -0.014129 0.063512 734 | episodes 0.008078 0.082640 -0.065570 -0.085393 -0.025303 735 | -US -0.096832 -0.076553 -0.052152 0.058771 0.001917 736 | Meet 0.086157 0.044854 0.047657 -0.041581 -0.002068 737 | close -0.030232 0.062009 0.039002 -0.097739 -0.079062 738 | eyes -0.059070 0.095488 -0.066651 0.057514 0.023659 739 | Wikileaks 0.004445 0.093285 -0.048756 -0.012419 0.038322 740 | Knockoff -0.020310 0.090673 0.056756 -0.074488 -0.054767 741 | That -0.038909 0.096085 -0.094116 -0.023546 0.091346 742 | Terrified 0.036247 0.058114 -0.057639 0.065697 -0.028180 743 | Birmingham 0.028092 0.085558 -0.017029 -0.015025 -0.076180 744 | Bolton -0.011764 0.081182 -0.035251 0.083194 0.046627 745 | Wanderers -0.046282 0.090529 0.031772 0.055346 -0.025952 746 | 1: 0.040329 0.011359 -0.036011 0.053342 -0.031508 747 | Cemetery -0.033271 -0.001341 0.087078 0.040500 -0.061208 748 | owner -0.057270 0.051549 0.060585 -0.097942 -0.044207 749 | trust 0.051700 -0.083384 -0.000817 -0.001881 0.067413 750 | rugby -0.048023 -0.035479 0.052506 0.003964 0.043290 751 | Foden 0.036545 0.069017 -0.077740 -0.062266 -0.064663 752 | cautioned -0.096778 0.034472 -0.078148 0.027330 -0.078756 753 | criminal 0.069127 -0.049984 0.024115 -0.002073 0.097441 754 | damage 0.070347 0.005398 -0.026264 0.006925 0.073015 755 | Ultra 0.020048 -0.088993 0.027471 0.002148 -0.057369 756 | diet -0.035416 0.052604 -0.016170 -0.086046 -0.058459 757 | advertisement 0.033083 0.063038 -0.012028 0.005273 0.019025 758 | Look 0.005527 -0.084697 0.099434 -0.081113 0.008457 759 | Moment 0.069202 0.072370 -0.061375 0.027043 0.078606 760 | Katie -0.050198 -0.035651 -0.083380 0.016212 -0.028876 761 | Holmes -0.048739 0.031179 -0.042230 -0.053213 0.066992 762 | Brit -0.085211 0.016387 0.023658 -0.092685 0.042447 763 | framer 0.002163 0.026092 0.028263 -0.018812 0.022853 764 | buried 0.077355 -0.004472 0.093388 0.031854 0.030999 765 | unmarked 0.022390 -0.000665 0.058016 -0.080052 -0.082640 766 | grave 0.007356 0.049403 0.077275 -0.059719 -0.076603 767 | outlaw 0.064389 -0.060124 0.041660 0.089167 -0.072370 768 | behind 0.092566 -0.038423 -0.003363 -0.078876 -0.061306 769 | Robin 0.047559 0.044527 -0.091778 0.096179 0.051165 770 | Hood -0.031210 -0.067871 0.092782 -0.023980 0.066887 771 | legend? -0.027702 -0.046878 -0.047671 -0.053388 -0.014108 772 | Betsey -0.092126 0.039883 -0.086824 -0.029412 0.043677 773 | Johnson, -0.095000 0.089964 -0.065656 -0.059194 -0.062348 774 | Karlie -0.099001 -0.063101 0.035839 0.028991 0.009592 775 | Kloss -0.002913 0.020556 -0.021326 -0.046085 -0.007303 776 | Teen -0.042060 0.080049 0.066107 0.095812 0.012393 777 | Their 0.041229 0.045887 0.097816 -0.019194 0.080229 778 | Prom 0.063705 -0.081888 -0.036319 0.093154 0.033242 779 | Pics 0.001221 0.030949 0.022779 0.095601 0.074642 780 | Netflix -0.007033 0.062293 -0.004275 0.079661 0.051751 781 | strikes -0.085384 -0.018508 -0.084262 -0.004449 -0.021068 782 | stream -0.061944 -0.001422 0.045102 -0.066520 -0.087352 783 | content -0.002598 -0.004977 0.015458 0.003502 0.075632 784 | while 0.084289 0.031369 0.040168 0.094872 -0.022898 785 | losing -0.091591 -0.097225 -0.082904 0.037546 0.088487 786 | others -0.083278 0.006069 -0.057528 -0.094761 -0.079815 787 | Arts -0.051858 -0.017836 -0.077356 -0.093755 0.019807 788 | It: 0.040609 0.066597 0.004382 -0.057691 0.009964 789 | TGIF 0.022095 0.020152 -0.058298 -0.077522 0.028697 790 | D&G; -0.081673 -0.045057 -0.038886 0.032991 -0.000976 791 | Other 0.091888 -0.070336 0.022065 0.057469 0.079662 792 | Sheen 0.072817 -0.048819 -0.001281 -0.030020 -0.003776 793 | Comes -0.067286 0.070344 0.084077 0.039579 -0.057794 794 | Dita -0.005134 0.054238 -0.051218 -0.063580 0.005522 795 | Von 0.050531 0.043749 -0.009231 0.100974 -0.072820 796 | Teese 0.055233 -0.031399 0.039654 0.058827 -0.039826 797 | ditches -0.098450 0.004020 -0.079161 -0.024650 0.031220 798 | siren 0.017205 0.084255 -0.068735 0.090716 -0.041847 799 | wears 0.047045 0.029362 -0.066344 0.078620 0.003293 800 | shapeless 0.048924 -0.052441 -0.072181 -0.028597 -0.013970 801 | retro 0.084222 -0.086430 -0.079160 -0.034477 -0.070456 802 | flat -0.079476 0.079559 -0.015526 -0.076855 0.092182 803 | heels 0.057772 0.085769 -0.060698 0.039924 -0.065941 804 | Crude 0.089426 0.100401 -0.049071 0.003968 -0.026293 805 | 30-month -0.079276 -0.087442 -0.083410 0.050171 0.007367 806 | high -0.045625 0.006265 0.015317 0.092286 0.028811 807 | growth, -0.052589 0.079825 -0.087989 -0.001635 0.092824 808 | weak 0.062115 0.057377 -0.087852 -0.070894 -0.007230 809 | $US -0.082163 0.022651 0.076834 0.083342 -0.033897 810 | Simon 0.049032 0.008027 0.035022 -0.007342 0.068698 811 | Hughes: -0.044034 0.019378 0.007012 -0.093212 -0.056000 812 | captain -0.066734 -0.095784 0.092949 -0.020558 0.045633 813 | Mahendra 0.086786 -0.093937 -0.023564 0.025116 0.081950 814 | Singh 0.051222 -0.087405 -0.078638 0.009730 -0.077689 815 | Dhoni -0.079788 0.032890 0.030414 -0.029796 -0.075991 816 | timed 0.095896 -0.091690 -0.004579 -0.039580 0.074721 817 | entire 0.068359 0.016389 -0.036844 0.014450 -0.057013 818 | campaign -0.032157 -0.086615 0.053339 -0.023993 0.050868 819 | perfection 0.038583 0.072945 0.099820 0.003002 -0.015242 820 | Are -0.008432 -0.065806 0.004030 0.034694 0.077448 821 | Kids -0.013249 -0.033840 -0.087203 -0.091638 -0.047002 822 | Overscheduled? 0.030721 0.100244 0.018467 -0.042731 -0.028323 823 | Parker: -0.036284 -0.015961 -0.098755 -0.095999 -0.025761 824 | Elway, 0.019937 -0.065096 0.095371 0.045967 -0.002541 825 | partners -0.073117 0.054224 0.066750 -0.007492 0.079087 826 | huddle -0.083860 -0.072696 -0.009808 -0.097524 0.007265 827 | Vail -0.024597 0.000642 0.035123 0.001690 0.064763 828 | restaurant -0.072877 -0.044446 0.085553 0.066667 0.014147 829 | creative 0.031485 -0.092096 0.019697 0.091260 0.069137 830 | interpretations -0.077534 -0.072483 0.068138 -0.022031 -0.026161 831 | Resolution 0.076573 0.000336 -0.084678 -0.066563 -0.092376 832 | 1973 0.016014 -0.080956 0.039067 -0.045959 -0.097724 833 | Concern -0.058235 -0.094633 -0.048787 -0.001965 -0.028217 834 | competition 0.033597 -0.014491 -0.045930 0.072121 -0.054954 835 | banking 0.005403 -0.080658 0.090329 0.055727 -0.019481 836 | Root -0.038029 -0.082573 -0.032349 -0.053593 0.082249 837 | family-centred 0.088491 0.090159 0.068521 0.063304 0.077092 838 | government, 0.053868 0.097491 -0.095758 -0.046853 -0.026066 839 | Jayalalithaa -0.075522 0.073753 -0.018956 -0.025503 -0.023070 840 | stations -0.092281 0.011666 0.047297 0.045127 -0.053436 841 | pose 0.002856 0.005009 0.039870 0.042534 -0.052715 842 | dilemma -0.020487 -0.009232 0.089107 -0.019529 0.024971 843 | Hydrogen -0.058532 0.013602 0.040142 -0.078998 0.094443 844 | ""to -0.010085 0.009992 -0.081688 -0.033872 -0.066298 845 | viable -0.069741 0.001497 -0.089413 0.088847 0.047580 846 | alternative -0.039641 -0.053682 -0.028815 -0.006786 -0.077879 847 | petrol"" -0.015197 0.018056 0.013748 0.026870 -0.091222 848 | thanks -0.102001 -0.082480 -0.025334 0.002617 0.093270 849 | process 0.027146 -0.041889 -0.075189 0.066877 0.075131 850 | makes -0.077457 -0.010845 0.013762 -0.069987 -0.001361 851 | behave -0.011348 -0.048563 0.077777 -0.064222 0.072913 852 | liquid -0.084221 0.023699 -0.062335 0.073918 -0.068344 853 | High-profile 0.054807 -0.009830 -0.067702 -0.016145 0.072048 854 | resigns 0.054270 0.035699 0.080807 -0.062246 -0.049719 855 | caught 0.034351 0.056687 -0.064932 0.018529 0.031636 856 | ""buying 0.081044 -0.057827 0.096842 0.075045 0.054000 857 | cocaine"" 0.071931 0.060914 -0.029560 -0.059972 0.059987 858 | Kate -0.089831 -0.041972 0.070118 -0.078735 -0.004137 859 | Moss -0.094875 -0.002946 0.085205 0.041474 -0.030830 860 | hologram 0.004053 0.069105 0.091513 -0.069383 0.031928 861 | she -0.068159 -0.014550 0.036361 -0.002884 0.070122 862 | pays 0.082424 0.098017 0.100329 0.073885 -0.018721 863 | tribute -0.079850 0.089615 0.032528 -0.015132 0.034597 864 | Alexander -0.025405 -0.009818 0.089692 0.095488 -0.055626 865 | McQueen 0.017541 -0.016751 -0.045841 0.045504 0.026270 866 | magazine 0.026587 -0.091738 0.021220 -0.060397 -0.082901 867 | shoot 0.037844 0.006779 -0.044461 -0.058821 -0.071142 868 | school 0.007775 -0.027430 -0.076842 0.005172 0.008482 869 | Top -0.020124 -0.094631 0.025663 -0.010109 -0.006769 870 | briefing 0.064107 -0.019829 0.038190 0.016607 0.007505 871 | Robert 0.014961 0.083824 0.070833 -0.050329 0.055061 872 | Marshall-Andrews 0.082702 0.009910 -0.034848 -0.067627 0.040010 873 | daughter -0.055711 0.070642 0.026457 0.095128 -0.065029 874 | Laura 0.057193 -0.003180 0.010929 -0.028948 0.051809 875 | embrace -0.026340 -0.079531 -0.016801 -0.001616 0.076558 876 | ""le -0.025211 -0.026620 0.037132 0.082168 0.085632 877 | vin -0.082108 0.041981 0.082782 0.062554 0.078139 878 | plastique’ 0.061010 0.036657 0.096444 -0.076823 0.090359 879 | review: -0.024645 -0.009533 -0.087099 -0.018880 0.086040 880 | haunted-house 0.047384 -0.100561 -0.091787 0.028935 0.056882 881 | tale 0.085618 -0.074112 0.011010 -0.049475 -0.007784 882 | ""Insidious"" 0.069363 0.042094 0.091697 0.015250 0.063159 883 | must 0.065421 0.024445 -0.053931 0.000580 0.091607 884 | make 0.041479 0.014668 0.044015 0.033456 -0.034210 885 | adder 0.027390 -0.023191 0.074254 0.052385 -0.083200 886 | count 0.002733 -0.018016 -0.019278 -0.080704 0.025801 887 | Nationals' 0.024933 0.057573 0.039878 0.006799 -0.076538 888 | challenge 0.000666 -0.062008 -0.054174 0.091558 0.079273 889 | influential 0.014929 0.038950 -0.050227 -0.062345 -0.074424 890 | seniors 0.034440 -0.045147 0.026355 -0.052697 -0.002909 891 | group -0.093578 -0.093530 -0.062224 -0.079476 0.013283 892 | NYPD 0.050977 -0.076209 0.088081 0.034577 -0.099844 893 | Comm. 0.020016 -0.047994 0.026134 -0.080701 0.081755 894 | Kelly: -0.074028 0.093854 0.070160 0.005058 -0.049734 895 | cop -0.072436 0.011573 0.076201 0.030117 -0.054243 896 | way -0.061846 0.033521 0.079963 0.061972 0.010134 897 | Towns 0.054862 -0.097155 -0.011250 0.041459 -0.029402 898 | Opposition 0.067702 -0.081562 -0.068364 0.071040 -0.022365 899 | Word 0.044429 -0.094261 -0.098240 -0.031640 -0.081178 900 | Mouth: -0.064269 -0.093108 -0.084225 -0.068779 0.063233 901 | What 0.031392 -0.002697 0.069799 -0.014724 0.084615 902 | hit, -0.038325 0.087964 0.073945 0.017762 0.075340 903 | miss -0.081626 0.019069 -0.019822 -0.037075 0.007071 904 | surprise 0.076156 -0.031551 -0.004693 -0.020367 -0.079321 905 | escapes -0.081942 -0.015022 -0.019012 0.087747 0.028722 906 | prison -0.004781 0.038149 0.063103 0.060195 -0.015080 907 | TWICE -0.047588 0.041497 -0.065683 -0.037693 -0.091503 908 | sends -0.063066 -0.072618 -0.034717 0.051853 0.007200 909 | faxes -0.044041 -0.025424 0.055677 0.082560 -0.023524 910 | ordering 0.030367 -0.070535 -0.055690 0.043703 0.095396 911 | Bashar 0.089391 -0.071563 -0.030270 -0.021983 -0.031890 912 | Safar -0.057899 0.001628 -0.032075 -0.066062 0.068437 913 | PM -0.001060 0.070765 -0.065345 -0.041645 0.059786 914 | Evelyne 0.054376 0.052937 0.012725 0.078104 -0.039280 915 | Politanoff: 0.084916 -0.082506 -0.090902 0.067227 -0.064908 916 | And -0.075270 -0.054065 -0.093156 -0.088469 0.074328 917 | Spain, -0.042342 -0.066082 0.095292 0.097902 0.085763 918 | Cristobal 0.009277 0.071872 0.049434 -0.026119 -0.040947 919 | At 0.028804 -0.016697 -0.047707 0.102872 0.057449 920 | de 0.073846 0.071941 -0.054631 -0.065260 -0.087633 921 | Rate -0.054145 -0.072650 -0.027123 0.018548 0.059345 922 | Unexpectedly 0.034215 -0.010281 -0.011412 -0.079973 -0.027669 923 | Drops 0.024647 0.094827 -0.066800 0.069509 -0.086752 924 | Two-Year 0.066234 -0.021787 0.097636 0.037309 0.041598 925 | Low 0.060792 -0.015855 -0.042942 0.024340 0.025177 926 | Launches 0.054676 0.007787 -0.092389 -0.003791 -0.037025 927 | Site -0.094416 -0.085808 0.074411 -0.001835 -0.045808 928 | ""NEVER! 0.017465 -0.011437 0.044217 -0.033469 0.097647 929 | He 0.022739 0.024591 -0.060955 -0.032817 0.039872 930 | lowlife 0.051901 -0.080646 0.073706 0.066781 -0.025941 931 | bully"" 0.053849 -0.004094 0.058254 -0.002996 0.063160 932 | attempted -0.043431 -0.075698 -0.093654 0.051141 0.029212 933 | Japan's -0.085640 -0.091032 0.045153 -0.016938 -0.049506 934 | 'radioactive 0.076288 -0.086645 -0.064404 0.059825 -0.029198 935 | particles' -0.065701 -0.006384 -0.000152 -0.069900 0.051286 936 | Moscow -0.063716 -0.004384 -0.072491 -0.028573 0.081067 937 | Cat -0.028396 0.052374 0.097465 0.021146 0.057822 938 | allergy -0.013320 -0.067301 0.029475 -0.008430 0.061943 939 | sufferers -0.025623 0.020833 -0.011359 -0.078997 -0.010444 940 | given 0.029181 -0.072755 0.053224 0.004110 -0.065344 941 | ray -0.081478 -0.038455 0.072949 -0.065526 0.056784 942 | hope -0.035501 0.050037 0.058444 -0.003297 0.058706 943 | vaccine 0.073591 0.028268 -0.079857 0.035577 -0.093313 944 | Coalition 0.021230 -0.066871 -0.091921 -0.008934 0.020510 945 | areas -0.080652 0.002274 -0.028437 -0.025519 -0.079500 946 | Khoms, 0.060121 0.044720 -0.070173 0.000986 -0.054332 947 | Arrujban 0.049055 -0.018555 -0.084301 0.000181 -0.001292 948 | -Libyan -0.020247 0.036748 -0.032568 -0.029545 -0.089973 949 | Mayor -0.027389 0.018273 0.077237 0.073392 0.007290 950 | Bloomberg's -0.023152 0.065456 -0.032312 0.001232 -0.042763 951 | approval -0.046992 0.009353 0.012646 0.003838 -0.076109 952 | falling 0.018672 0.046536 -0.048350 -0.008712 -0.031148 953 | fast, -0.087084 -0.043270 0.056870 0.022013 0.034043 954 | Marist 0.079289 0.003848 0.044704 -0.029388 -0.060316 955 | College -0.014709 -0.072704 -0.050625 -0.071548 -0.090248 956 | poll 0.075487 0.042207 -0.087399 -0.054392 -0.038182 957 | finds 0.033603 -0.089555 0.052660 0.066777 0.002269 958 | NoSQL -0.008755 -0.059528 -0.075706 0.025847 -0.033755 959 | Helping -0.007363 -0.069514 0.056138 0.009263 -0.039795 960 | Allay -0.042373 0.036730 0.090825 -0.067489 -0.055771 961 | Seattle's -0.056464 -0.039588 -0.098629 -0.007076 0.039067 962 | Fears -0.050981 0.028231 0.018757 -0.093560 -0.075667 963 | Insider 0.014881 0.062009 0.098959 0.082977 0.074097 964 | Athena -0.040816 -0.031150 0.047054 0.073465 0.092922 965 | Calderone -0.074827 0.043288 0.053629 0.065733 -0.003148 966 | 111, 0.049039 -0.045113 0.000380 -0.086555 0.026348 967 | 98 -0.004977 0.063620 0.073640 -0.055654 0.074921 968 | Defense -0.002280 -0.005213 0.049150 -0.014789 -0.043552 969 | rests -0.062367 -0.011442 -0.067302 0.004645 -0.010573 970 | Cuban 0.070789 -0.012889 -0.036940 -0.036485 -0.032846 971 | ex-CIA -0.074657 0.022967 0.003589 0.089409 -0.007366 972 | agent's -0.042161 0.051228 -0.095162 0.037702 -0.049758 973 | perjury -0.063260 -0.061417 -0.079137 0.060263 -0.070928 974 | filed -0.100998 -0.047533 0.024646 0.024857 0.000614 975 | formal 0.077842 0.085623 -0.084478 -0.070366 0.068914 976 | complaint -0.043916 0.041677 -0.004897 -0.066487 -0.050038 977 | search 0.059580 0.098152 0.055166 -0.074873 0.045714 978 | giant's 0.029160 -0.033061 -0.070692 0.027938 -0.029990 979 | reponse 0.035290 -0.019459 0.061529 0.003617 -0.037886 980 | Scott -0.066346 0.017532 0.033190 0.099456 -0.044141 981 | Brown -0.093334 -0.065510 -0.000454 -0.071507 0.029449 982 | Criticizes 0.091580 0.089088 -0.026329 -0.093479 -0.010647 983 | ""Irresponsible"" 0.054126 -0.035351 0.084247 -0.010837 0.025836 984 | gag -0.077980 -0.007246 -0.043212 0.048476 -0.032983 985 | inevitably 0.070913 -0.006218 -0.080847 -0.023673 0.027831 986 | turned -0.059093 -0.008357 -0.065044 0.017516 0.038517 987 | reality 0.096702 -0.064821 -0.031387 0.091866 0.029605 988 | using 0.006889 0.005770 0.072278 -0.057819 -0.066373 989 | Kinect 0.033191 0.033181 -0.041346 -0.057878 -0.094727 990 | convicted -0.006938 -0.031740 -0.070578 0.001641 0.090008 991 | kidnapping 0.064543 -0.057582 0.063006 -0.027052 -0.020249 992 | slain 0.077247 0.093873 -0.038979 0.035084 0.047193 993 | trafficker 0.042147 0.097102 0.045707 0.007288 -0.083815 994 | Disaster -0.019655 -0.094604 0.061571 -0.044711 -0.043851 995 | toll -0.063509 -0.036698 -0.092664 0.008500 0.038597 996 | auto, 0.065844 0.039928 -0.001967 -0.027568 0.014440 997 | department -0.071238 -0.039501 -0.039147 -0.031722 0.084286 998 | store 0.033338 -0.071288 0.080617 0.052967 0.017460 999 | Barbour's -0.035878 -0.064955 -0.051231 0.058233 0.073244 1000 | Afraid -0.032943 0.049414 -0.007093 0.073773 -0.024382 1001 | He'll 0.006134 0.016480 -0.054736 -0.083538 0.086756 1002 | President -0.008670 0.060165 0.053610 0.046952 -0.032914 1003 | MLB 0.028213 0.002140 -0.001610 0.010344 0.027478 1004 | inquiry 0.042585 0.055781 -0.032777 0.077103 -0.083995 1005 | quarantine -0.010536 -0.087925 -0.021729 -0.029141 0.069269 1006 | watchdog 0.005671 -0.012222 0.013815 0.038335 -0.002239 1007 | detects -0.101292 -0.004054 0.061293 -0.088450 -0.033330 1008 | 10 -0.047482 -0.089274 -0.067842 -0.004239 0.017860 1009 | contamination -0.065025 0.055185 0.044565 -0.033061 0.068923 1010 | Dennis 0.037824 0.079535 0.023294 0.015007 0.033417 1011 | headed -0.011283 0.030028 0.066307 -0.006445 0.077859 1012 | Jean -0.053309 0.092600 -0.056275 -0.006800 0.004172 1013 | Paul 0.028667 0.069023 0.096188 0.020923 -0.076532 1014 | Gaultier's -0.056292 0.095433 0.051430 0.083759 0.005230 1015 | talking -0.031058 -0.014765 -0.000288 -0.057885 0.072346 1016 | head 0.002114 -0.035679 -0.084324 0.096886 0.055744 1017 | Donald -0.015541 -0.011585 0.086460 0.000463 -0.054089 1018 | Trump -0.032229 0.022408 0.069376 -0.065891 0.003308 1019 | Lands -0.090394 0.068692 0.087384 -0.061047 0.092358 1020 | Fox -0.083298 -0.077141 -0.052955 0.043226 -0.049276 1021 | News 0.060602 0.088609 0.097227 0.034507 -0.004609 1022 | Gig 0.074556 -0.086720 0.052882 0.013979 -0.026618 1023 | Device 0.095111 -0.083864 0.005888 -0.021417 -0.100865 1024 | Uses 0.002704 0.086342 0.044261 -0.059632 0.012114 1025 | Carbon -0.046550 -0.081150 0.011600 0.068775 0.031397 1026 | Nanotubes -0.066612 -0.019848 0.015011 0.088459 0.042571 1027 | Catch -0.032952 0.026525 -0.055087 0.016333 -0.071828 1028 | Tumor -0.013025 -0.017446 0.075768 -0.060880 0.017913 1029 | Cells 0.070412 0.010473 -0.002720 0.040304 -0.038467 1030 | Quickly 0.035320 0.064405 -0.007711 -0.027642 -0.095828 1031 | Rockets 0.055971 -0.068013 -0.033440 -0.087709 0.097082 1032 | bombard -0.023884 -0.073376 0.041537 0.058644 -0.050983 1033 | slumping -0.072499 0.025097 -0.055194 -0.080012 -0.026635 1034 | Spurs -0.015594 0.035382 0.066137 0.022338 0.025063 1035 | Report: -0.079659 0.082018 -0.091083 0.033891 -0.033885 1036 | UConn 0.019408 0.037911 0.023458 -0.010341 -0.055676 1037 | recruit 0.092229 -0.076578 -0.021312 -0.038485 -0.075554 1038 | talk -0.079040 -0.017618 0.070155 0.046897 0.035563 1039 | Ditches 0.072650 0.051508 0.056475 0.086639 0.065780 1040 | QuickBar -0.068341 0.080372 -0.077442 0.026945 0.097073 1041 | A.K.A. 0.059784 -0.038864 -0.008396 -0.039904 -0.029154 1042 | ""DickBar"" -0.037889 -0.017831 0.023746 -0.019231 0.045057 1043 | Non-alcoholic -0.021916 0.074837 -0.034340 -0.077055 0.001548 1044 | fatty -0.011135 -0.069189 -0.012346 0.080416 -0.006942 1045 | liver 0.005429 -0.023235 -0.025849 -0.035885 -0.049814 1046 | reach 0.046237 -0.041767 0.064934 -0.035736 -0.024348 1047 | epidemic 0.092446 -0.003438 0.005359 0.079482 -0.049118 1048 | status 0.090566 0.063716 -0.038756 0.063818 -0.006673 1049 | Five-run -0.083480 0.080299 0.034363 -0.001796 0.080125 1050 | carries -0.096442 -0.032854 -0.006840 -0.082594 -0.054573 1051 | Cubs -0.089799 0.088879 0.005993 0.008273 0.050583 1052 | Cardinals 0.078900 0.073282 0.032668 -0.066518 -0.021659 1053 | hold 0.077806 -0.018314 -0.057783 0.032287 0.020072 1054 | putting -0.092815 -0.093582 -0.086617 0.086908 0.053542 1055 | Holliday -0.018157 0.059152 -0.023747 -0.024553 0.092062 1056 | DL -0.063762 0.004447 0.056527 0.001828 -0.045668 1057 | tops -0.056757 -0.013768 0.027330 0.074418 -0.003920 1058 | cell 0.052050 -0.013308 0.018475 0.089925 -0.077638 1059 | phones 0.024650 0.085744 0.029271 0.041409 0.074100 1060 | February, -0.086219 0.098533 -0.076110 0.015227 0.060095 1061 | continues -0.008809 -0.047155 -0.089840 -0.035650 -0.069734 1062 | surge -0.019335 -0.079891 0.097062 0.054017 0.072507 1063 | Mamet -0.008622 -0.075102 0.065336 -0.026816 -0.089222 1064 | Anarchist"" 0.016459 0.076748 -0.044719 -0.017948 0.002703 1065 | year 0.066304 0.055658 0.063174 -0.021375 -0.000323 1066 | William 0.012736 -0.089805 0.074901 -0.090659 -0.020396 1067 | Hague -0.060338 0.017141 0.079295 0.073643 0.051648 1068 | statement: 0.060054 0.078868 -0.058514 0.037529 0.027413 1069 | Diplomats 0.035260 -0.008059 0.043567 0.055609 -0.083799 1070 | expelled -0.087818 -0.031686 0.035875 0.060841 0.015911 1071 | embassy 0.092637 0.060524 -0.037507 -0.033526 0.026820 1072 | Bye, -0.004824 0.052760 -0.064778 -0.082885 -0.012097 1073 | Bye -0.023668 0.051696 -0.040421 -0.059170 -0.036604 1074 | Hello -0.029321 0.035370 0.029050 -0.010995 -0.068651 1075 | Cloud! 0.085908 0.086086 0.024192 0.097806 0.085292 1076 | NASA 0.082220 -0.071601 0.032908 0.004946 0.015305 1077 | space -0.081069 0.095047 -0.007890 0.052357 0.005433 1078 | inventions 0.079874 -0.017352 -0.069354 0.076028 -0.063116 1079 | benefit 0.074735 0.095956 0.095567 -0.029064 -0.085993 1080 | our -0.061150 0.077904 -0.074599 -0.048010 -0.010495 1081 | lives 0.083057 0.050756 0.020629 0.094946 -0.016059 1082 | Earth 0.085683 0.086907 0.001615 0.080270 0.065435 1083 | bank -0.083467 0.049749 0.058478 -0.015058 0.068019 1084 | creditors, 0.063154 -0.066777 0.065723 0.026452 -0.092812 1085 | ECB 0.094044 0.092217 0.014411 0.064547 0.021222 1086 | Half 0.002082 0.009392 -0.063628 0.058282 0.062435 1087 | Million -0.098099 0.038144 0.020237 -0.072180 -0.038584 1088 | Die -0.063680 -0.019228 -0.058039 0.040882 -0.081807 1089 | Smoking -0.044100 0.098121 0.054540 0.050056 0.087497 1090 | Yearly 0.024292 0.025012 -0.050648 0.071087 -0.060257 1091 | Pistons -0.048334 -0.018814 -0.034437 -0.043537 0.080326 1092 | retires 0.048631 -0.048644 0.064276 -0.088163 -0.079522 1093 | Rodman's -0.093766 0.044498 -0.053572 0.024626 -0.084108 1094 | No.10 0.044590 0.008699 -0.070136 0.030067 0.095441 1095 | jersey -0.030434 -0.070724 -0.079184 -0.005755 0.024866 1096 | America? 0.074509 0.001008 -0.091447 -0.096536 0.016913 1097 | Couple -0.042956 0.019082 -0.095514 0.039698 -0.026336 1098 | ""forced 0.010499 0.047230 0.097573 -0.030574 -0.023719 1099 | son, 0.033732 -0.020423 -0.051275 0.040325 -0.057123 1100 | 7, 0.091265 -0.090946 0.044286 -0.002797 -0.013542 1101 | then 0.021744 -0.085244 0.034004 -0.071569 0.093544 1102 | patrol 0.027162 -0.059648 0.012209 -0.093489 0.070961 1103 | it"" -0.014379 0.061826 0.081996 0.013223 0.080952 1104 | Capsules -0.098286 0.068087 0.093191 -0.030883 -0.035445 1105 | Donna 0.097770 0.075477 -0.059815 -0.003205 -0.094730 1106 | Karan -0.027813 0.048935 0.006716 0.012265 0.068181 1107 | redux 0.084199 0.098249 0.026383 0.073963 0.079314 1108 | essential -0.081870 0.071291 0.033599 -0.051442 0.015041 1109 | luxe -0.047106 -0.068244 0.035752 -0.012347 -0.092078 1110 | basics 0.059217 0.094103 0.021085 -0.055082 0.096727 1111 | Jimenez -0.041402 0.047736 0.061015 -0.000792 -0.093600 1112 | His -0.063632 0.083948 -0.042520 -0.017101 -0.004406 1113 | Usual 0.043271 0.079518 -0.079546 -0.076765 0.063621 1114 | Stuff -0.023637 -0.002302 0.020299 0.084055 0.077498 1115 | 'Westside -0.098951 -0.083644 -0.078480 -0.091076 0.005278 1116 | Rapist' 0.039347 0.071075 0.034884 0.086467 -0.013482 1117 | admits 0.077523 -0.092859 0.055209 0.042813 0.042230 1118 | strangling -0.098345 0.055393 0.042083 -0.062507 0.067541 1119 | death, 0.077881 -0.046774 0.076680 0.026559 0.007455 1120 | real -0.066005 -0.095071 0.008935 -0.008828 0.033026 1121 | number 0.026379 -0.020205 0.091203 -0.094210 -0.090918 1122 | Katy -0.070941 0.033462 -0.060365 0.083718 -0.007830 1123 | Perry 0.085493 -0.045151 -0.027391 0.002085 0.055206 1124 | copying 0.034265 -0.076668 -0.062744 0.032652 -0.072532 1125 | ripping 0.074205 -0.059179 -0.029941 0.015606 -0.010906 1126 | Madonna! -0.044460 -0.016096 -0.006801 0.097115 -0.017022 1127 | Unveiled: 0.008922 0.023596 -0.054614 0.033050 0.047014 1128 | Thirty -0.069659 0.057471 -0.024377 -0.044501 0.065716 1129 | big, 0.064705 -0.068868 -0.006444 -0.034025 0.063802 1130 | fat -0.055008 -0.098604 -0.003989 -0.026715 0.095475 1131 | celebrity -0.079410 0.066479 -0.077400 0.052106 -0.039606 1132 | weddings -0.009259 -0.052650 -0.072761 -0.006598 0.065892 1133 | Colors: 0.071660 -0.056738 -0.091436 -0.016994 0.071274 1134 | Bathing 0.007256 0.026065 -0.097637 -0.034180 -0.053843 1135 | An -0.008183 -0.085670 -0.080978 0.062444 -0.071924 1136 | Entirely 0.070911 -0.081604 -0.052649 0.078428 0.078911 1137 | Light 0.082017 -0.022235 0.010252 0.078298 -0.083635 1138 | Inadvertently -0.023531 -0.086181 0.046249 0.037314 -0.089702 1139 | Revealed 0.090665 0.088718 0.080657 -0.071296 -0.006274 1140 | 5""s -0.066982 0.009810 -0.054646 -0.033151 -0.072272 1141 | 8MP -0.063894 -0.091974 -0.033436 0.083355 -0.092409 1142 | Camera -0.049680 0.037833 0.040901 0.028305 0.005107 1143 | 1-India -0.028422 -0.006332 0.003130 0.048557 0.024904 1144 | ex-minister, 0.083745 0.086969 -0.013053 -0.095921 0.077123 1145 | Reliance 0.025154 -0.063988 -0.039819 0.011699 -0.018400 1146 | ADA -0.026867 0.010564 0.025296 0.058532 -0.067601 1147 | telecoms 0.020630 0.058941 -0.080166 0.092347 -0.037709 1148 | graft 0.002966 -0.057164 -0.085525 -0.046719 -0.070823 1149 | Forgiveness -0.024415 -0.033675 -0.032034 0.095475 -0.096417 1150 | sins 0.000725 -0.087118 0.098726 0.019534 0.079245 1151 | Pollution 0.039153 -0.050745 0.025196 0.045536 0.063979 1152 | Affects -0.096458 0.045381 -0.039902 -0.098056 -0.086259 1153 | Troops 0.028762 0.046351 -0.060088 0.004159 -0.031273 1154 | Iraq: 0.023778 0.004756 0.099159 -0.051372 0.021240 1155 | Deadly 0.038138 0.069134 -0.004587 0.033538 -0.058755 1156 | Terrorists? 0.004772 0.087835 0.011360 -0.081850 0.062431 1157 | United's 0.077452 0.089273 -0.008154 -0.053590 -0.093486 1158 | Wayne -0.053296 -0.007363 0.043239 -0.032131 0.092491 1159 | apologises 0.035455 0.046435 0.087353 -0.023594 0.031511 1160 | swearing 0.044887 -0.078689 0.085161 0.013518 0.073741 1161 | outburst -0.007807 -0.002658 -0.062233 0.042371 0.089678 1162 | ouster 0.056147 0.012339 -0.094208 -0.072973 0.095906 1163 | would -0.065417 -0.032041 -0.089570 -0.094068 0.038798 1164 | protection -0.036199 0.040390 -0.056437 -0.075180 0.057279 1165 | civilians -0.034415 -0.059281 -0.090228 -0.006159 -0.056153 1166 | Giants 0.027530 -0.015700 -0.070933 0.070187 0.074179 1167 | lose -0.063793 0.061456 -0.007043 -0.096744 0.042462 1168 | Dodgers -0.050932 0.021303 0.022210 -0.029842 -0.034002 1169 | again, -0.095156 -0.000543 0.078499 0.006799 0.086433 1170 | Lowly -0.073329 0.051624 -0.026493 -0.067681 -0.060827 1171 | Avs -0.016716 -0.068511 0.069584 0.036973 -0.000995 1172 | Coyotes -0.059881 0.006929 -0.056245 -0.010134 -0.101195 1173 | shootout -0.023631 -0.010019 -0.086563 0.059585 0.046909 1174 | tournament: 0.055566 0.025450 -0.026416 -0.013145 -0.072837 1175 | Surge -0.075739 -0.004763 0.031403 -0.008065 0.077197 1176 | fuels -0.099107 0.027600 -0.024936 -0.029482 -0.068863 1177 | economic -0.071004 -0.064445 0.099225 -0.054516 -0.040796 1178 | optimism 0.048714 -0.045852 0.053804 -0.017177 -0.054840 1179 | Marlins 0.006607 0.073387 -0.007331 0.024334 -0.034460 1180 | ace 0.004235 0.001681 -0.032867 -0.060203 -0.037789 1181 | shuts -0.052631 -0.032421 0.054609 -0.097336 -0.074405 1182 | Mets -0.055569 -0.077186 -0.080385 -0.031170 -0.068039 1183 | parties 0.008533 -0.052588 0.027820 -0.056722 -0.020465 1184 | Cote 0.079968 -0.086010 0.093252 0.005879 -0.002928 1185 | d'Ivoire 0.070071 0.026320 -0.083032 0.017915 0.052634 1186 | exercise -0.053799 -0.081624 -0.075835 0.077413 0.024370 1187 | restraint -0.019030 -0.097657 0.006437 0.023513 -0.039589 1188 | Ethiopia: 0.045971 0.018465 0.012074 0.063598 0.066452 1189 | claiming -0.004693 0.011380 0.067685 -0.055387 0.021246 1190 | Jewish -0.081917 0.049140 -0.006616 0.096932 -0.100672 1191 | clashes -0.007159 0.040253 -0.003158 -0.024334 0.043195 1192 | Employment 0.088243 -0.089293 -0.094549 0.088598 0.011503 1193 | joke 0.075153 -0.036994 0.001602 -0.076290 -0.104539 1194 | added 0.027400 -0.090383 0.008260 -0.017695 -0.013260 1195 | Notable -0.047283 0.053275 -0.048703 -0.086028 -0.023743 1196 | Quotable -0.058007 0.006915 0.013747 0.017384 -0.052997 1197 | Parole -0.044267 -0.005296 0.040918 -0.048021 -0.036135 1198 | rule -0.010838 0.014367 0.043869 -0.083411 -0.081338 1199 | frees -0.007985 -0.073084 -0.064223 0.088684 0.014770 1200 | two -0.040400 0.094385 0.019017 0.056482 -0.049345 1201 | 17 0.016586 -0.031828 -0.080003 -0.053752 -0.040762 1202 | Cast 0.075354 -0.041612 -0.094269 0.025851 -0.084186 1203 | B.C. 0.003103 0.001793 0.065649 -0.053398 -0.047763 1204 | ""Twilight"" 0.094972 0.059977 -0.065715 -0.094017 -0.092358 1205 | wedding 0.032851 0.084753 -0.019058 -0.088296 -0.058218 1206 | scene 0.028919 0.096051 -0.003879 0.036600 -0.068380 1207 | Justin 0.097610 -0.012116 0.008972 -0.024297 -0.059189 1208 | Bieber 0.060062 -0.079257 -0.019709 -0.042934 0.000743 1209 | Ashton -0.023276 -0.050105 -0.050702 -0.079408 -0.025901 1210 | Kutcher: 0.004284 -0.038926 -0.095327 -0.099601 0.006960 1211 | buddy -0.034042 0.020854 0.045284 -0.004267 0.064678 1212 | ever? 0.026363 0.020793 -0.092105 -0.079216 0.004321 1213 | Never 0.048094 -0.011851 -0.074446 -0.029236 0.042301 1214 | Stopped 0.071801 0.071158 -0.026071 0.039759 -0.051481 1215 | v. 0.007047 0.036535 -0.098260 0.027324 -0.092452 1216 | Bush 0.017450 -0.084193 -0.054640 0.087744 0.001705 1217 | Let -0.080030 0.088260 -0.003138 0.015684 0.071739 1218 | Him 0.091039 0.038377 0.010162 -0.050136 -0.027335 1219 | Run -0.033608 0.033443 0.055528 0.050821 -0.034556 1220 | President? 0.014568 0.002594 -0.032675 0.062754 -0.004290 1221 | Illinois -0.090864 -0.011024 -0.097682 -0.051589 -0.025719 1222 | won't 0.056700 -0.024641 -0.046722 -0.073029 -0.091126 1223 | pursue 0.003351 0.011447 -0.030036 0.051333 -0.091799 1224 | penalty 0.084158 0.054661 0.006841 -0.028411 -0.025930 1225 | slaying -0.015546 -0.015942 0.061238 0.009134 0.002907 1226 | Builds 0.055731 -0.033015 0.024481 0.064366 -0.012429 1227 | Panic -0.072195 -0.040318 -0.079445 0.088036 0.022769 1228 | Button 0.040502 0.081562 0.003750 0.060624 0.069123 1229 | LVMH -0.054786 0.083641 -0.029644 0.079817 0.007292 1230 | Moves 0.019422 -0.009999 -0.040914 0.075310 -0.056619 1231 | Forward -0.098496 0.077893 -0.075008 0.017360 -0.074936 1232 | Gehry 0.064553 -0.047581 -0.012553 -0.073041 0.070021 1233 | Politics -0.005418 0.047448 -0.037103 0.000370 -0.044453 1234 | Weekly 0.005264 -0.020172 0.088922 0.061794 -0.022053 1235 | podcast: 0.059417 -0.087497 0.025399 -0.068382 0.075390 1236 | Ed -0.077213 0.034138 0.045754 0.011573 0.001908 1237 | Milband's -0.067429 -0.022525 -0.014202 -0.001444 0.008852 1238 | anti-cuts 0.056510 0.002662 -0.062223 0.007210 -0.076579 1239 | launches 0.085029 0.054385 -0.075897 0.068080 -0.093630 1240 | national 0.012012 0.029771 0.037384 0.017115 0.088283 1241 | survey -0.041697 0.065657 -0.055103 0.006847 0.035749 1242 | ""red -0.054056 -0.020632 0.074606 -0.033807 0.077310 1243 | resources"" -0.061261 -0.049759 0.044727 0.040585 -0.001369 1244 | protect -0.093261 -0.043436 -0.027787 0.078551 -0.018367 1245 | martyr -0.078165 -0.050055 -0.050172 -0.092722 -0.072746 1246 | memorial 0.043918 0.090250 0.052862 -0.030490 0.011701 1247 | facilities 0.022198 -0.027353 0.015715 0.066556 0.087157 1248 | raid -0.055882 -0.058619 0.048459 0.025753 -0.099314 1249 | Tripoli, 0.034868 0.030475 -0.038979 0.020388 0.086952 1250 | explosions 0.085908 -0.092778 -0.028652 0.020281 -0.046656 1251 | southeast 0.056255 0.043603 -0.084843 -0.011119 0.004591 1252 | suburb: 0.044081 -0.078016 -0.035470 0.069545 -0.035476 1253 | witness 0.045568 -0.017983 0.027454 0.046232 -0.025720 1254 | Arrest -0.039273 -0.041256 -0.009768 -0.059341 0.021028 1255 | severed 0.029250 0.077728 0.071426 -0.057658 0.046691 1256 | arm -0.069840 0.070311 -0.010162 -0.045675 0.083226 1257 | lake -0.081723 0.027498 -0.085458 -0.095005 -0.089373 1258 | Specialists 0.056652 -0.059385 0.093029 -0.068927 -0.026018 1259 | stats 0.070689 0.052171 0.095885 -0.085805 0.056763 1260 | self-injury -0.034532 0.012020 -0.013136 0.084734 0.021844 1261 | alarming -0.025213 0.032200 -0.047277 -0.092857 0.020201 1262 | court 0.072605 0.029363 -0.080565 0.031653 0.086383 1263 | tosses -0.073037 0.062110 0.075490 -0.090310 -0.006555 1264 | man's 0.070569 -0.020291 0.015029 0.007885 0.077924 1265 | overhaul -0.023616 -0.088767 -0.090368 -0.008703 -0.033324 1266 | NJ 0.086278 0.077754 0.004458 -0.057449 -0.020325 1267 | probation 0.096470 -0.086295 -0.033660 -0.079280 -0.017589 1268 | pleading 0.011568 -0.013634 -0.003837 0.093655 0.075433 1269 | lesser 0.042064 -0.018253 0.042989 0.052165 0.008505 1270 | offense 0.082022 0.073543 -0.006076 0.091029 -0.002542 1271 | cricketers -0.078417 -0.077173 -0.059913 0.011000 0.080017 1272 | complete -0.100291 -0.048300 0.085901 -0.040942 -0.047373 1273 | epic -0.071736 -0.049667 -0.033549 -0.039865 -0.025486 1274 | journey -0.037586 -0.022619 0.088840 -0.048480 -0.013421 1275 | exultant -0.094482 0.008011 -0.041733 -0.062431 0.041788 1276 | Mumbai -0.046155 0.003447 0.019381 -0.035741 0.010353 1277 | Dov -0.091515 -0.030570 0.040609 0.020066 -0.097356 1278 | denies 0.030152 0.082679 0.075910 -0.096460 0.034994 1279 | Orders -0.004049 0.046622 -0.031794 0.089365 0.018296 1280 | Boeing 0.043135 -0.099682 -0.097506 -0.060458 0.093942 1281 | Maritime 0.017213 0.091233 -0.050373 0.023786 0.094639 1282 | Planes -0.046698 -0.006357 -0.013854 -0.020483 -0.072982 1283 | dress: -0.011959 -0.074033 -0.028742 -0.090304 -0.034264 1284 | calf-length -0.037066 -0.023928 0.075671 -0.032082 -0.022637 1285 | skirt 0.014484 0.054674 0.078907 0.018035 0.052595 1286 | basketball -0.082070 -0.096399 -0.058424 -0.049096 -0.089666 1287 | coach -0.006694 -0.056313 -0.036169 0.023224 -0.040594 1288 | Finch, 0.030708 -0.020566 0.093571 0.021898 0.060298 1289 | Tigers 0.072884 -0.059463 -0.066048 -0.017992 -0.019133 1290 | 6 -0.033998 0.095229 -0.041385 0.035558 -0.081979 1291 | tournaments, 0.098285 0.091482 -0.018478 0.007864 -0.013546 1292 | dies -0.049099 0.073864 -0.013262 -0.009569 -0.092895 1293 | 60 -0.060342 0.028542 -0.020057 -0.031488 -0.094778 1294 | Tax -0.074173 0.058737 -0.041455 0.018298 0.088701 1295 | Deal -0.079565 -0.039770 -0.090137 0.099877 0.093827 1296 | Creates 0.017848 0.039924 -0.006381 0.062629 -0.021492 1297 | Classic -0.088234 0.040412 0.064602 0.060418 -0.026583 1298 | Row -0.082926 -0.023810 -0.023292 -0.080075 -0.014781 1299 | Simple -0.068800 -0.067602 0.091424 0.053941 -0.033086 1300 | injection 0.054375 -0.077390 0.007339 0.011136 0.087703 1301 | cure 0.098505 -0.050640 0.068225 -0.089323 0.086434 1302 | cat -0.049475 -0.047025 -0.072566 -0.037438 -0.049786 1303 | allergies 0.054713 -0.023638 -0.003862 0.071571 -0.054123 1304 | Disgraced -0.071178 0.002520 -0.061862 -0.031869 -0.031689 1305 | Dave 0.011136 0.032382 -0.083255 0.013139 0.036737 1306 | Attwood 0.060571 0.085640 0.055151 -0.092069 0.087418 1307 | come -0.017975 -0.090217 0.058744 0.030278 0.081392 1308 | ban 0.020081 -0.097371 0.060025 -0.066704 0.043235 1309 | stamping -0.012291 0.067036 0.028679 -0.086938 0.022751 1310 | Bilt 0.078341 0.058284 -0.072151 0.021087 0.001214 1311 | shelves -0.038779 -0.095458 0.060826 -0.026277 0.039523 1312 | LSE -0.094283 0.061826 0.081548 0.033627 0.023243 1313 | issue, -0.071097 -0.047262 0.013810 -0.099886 0.030184 1314 | sees 0.048287 -0.068034 -0.066014 -0.016828 -0.090946 1315 | valuation -0.030414 -0.061077 0.056368 -0.097185 -0.096154 1316 | Richard 0.045397 -0.089582 -0.004373 0.020436 -0.005282 1317 | Feynman, 0.064937 -0.061011 -0.020148 0.035543 -0.072447 1318 | Thinker 0.062533 -0.014518 -0.076270 0.025184 -0.031789 1319 | Boulevard -0.021148 -0.007553 -0.010502 -0.057302 -0.040249 1320 | Saturday 0.037765 -0.013905 0.016765 -0.081847 0.017149 1321 | interview: -0.071075 -0.015341 -0.014294 -0.036638 -0.007448 1322 | Pearl -0.023407 -0.013925 0.072706 0.049743 0.053889 1323 | Daisy 0.086087 -0.036848 -0.092306 0.009112 0.031753 1324 | Lowe -0.033308 -0.083648 0.071020 0.032202 0.008377 1325 | Gloucestershire 0.001138 -0.051936 -0.069357 -0.033095 0.044234 1326 | 'naked -0.091704 0.027810 -0.078187 0.007616 0.033792 1327 | gardening' 0.084140 0.056969 -0.038156 -0.006878 0.091377 1328 | Getting 0.012170 0.086418 0.094102 -0.071646 -0.082604 1329 | Work: -0.073943 -0.098501 -0.005390 0.027653 -0.082213 1330 | Employers -0.089189 0.089605 -0.047942 -0.052542 -0.042052 1331 | Hiring, -0.033674 -0.088797 0.004364 0.040648 -0.094379 1332 | Labor -0.078352 -0.046248 -0.032409 0.039950 -0.089685 1333 | Numbers -0.038693 -0.053430 -0.015341 0.010269 0.010141 1334 | Michele -0.079390 -0.011770 -0.101363 0.012558 -0.046945 1335 | Bachmann -0.049636 0.060637 0.006827 -0.092094 -0.014558 1336 | Ambitions 0.077066 0.081412 0.004656 -0.076397 -0.041838 1337 | Could 0.080604 -0.025627 0.055354 0.021590 -0.051638 1338 | Lead 0.033491 0.071935 -0.033967 0.039800 -0.029397 1339 | ""Rocky -0.004834 0.084680 -0.037032 -0.042348 -0.094115 1340 | Crusade"" -0.029776 -0.051099 -0.012949 0.035443 0.079231 1341 | Northampton 0.011160 0.033556 -0.003952 -0.037220 0.082796 1342 | Saints 0.058813 0.092534 -0.071787 0.033386 0.054481 1343 | Sale -0.024598 -0.087563 -0.029921 -0.029648 0.007970 1344 | Sharks 0.040756 0.086452 -0.086737 -0.036301 0.019725 1345 | 24: 0.006771 -0.075615 -0.037936 -0.084065 0.062800 1346 | Toxins 0.045821 -0.047343 -0.074259 -0.028242 0.012835 1347 | baby -0.014961 0.095885 0.005544 0.084270 -0.087292 1348 | food -0.027635 -0.035113 0.040337 0.050748 -0.105421 1349 | might 0.010471 0.041429 -0.045453 0.098640 0.089007 1350 | affect 0.067039 -0.009609 0.098070 -0.074153 -0.020438 1351 | hormones: -0.088721 0.053155 -0.042687 0.071478 0.015348 1352 | Sixers 0.040981 -0.069325 0.043256 -0.037504 -0.009849 1353 | 115, -0.056264 0.080302 0.071202 0.077690 -0.063503 1354 | Nets -0.101640 -0.078727 -0.031685 -0.018009 -0.043142 1355 | 90 -0.092671 -0.050493 0.012321 0.093626 -0.098701 1356 | comedy 0.045367 -0.068665 -0.093942 -0.053805 0.088244 1357 | Ban -0.093638 0.046088 -0.009503 0.091073 0.087981 1358 | stresses 0.091385 -0.092841 0.076150 -0.013352 -0.109194 1359 | concern -0.043093 -0.079989 -0.100397 -0.025001 -0.095264 1360 | stalled -0.071506 0.054095 0.088405 0.020904 -0.086845 1361 | peace -0.095952 -0.101885 -0.070846 0.032427 -0.045118 1362 | phone 0.023903 -0.067703 0.005152 -0.084421 -0.081902 1363 | premier -0.046932 -0.088061 -0.039475 -0.039851 -0.068332 1364 | joins -0.028996 -0.055869 0.015330 -0.056022 0.072403 1365 | ranks 0.044028 0.020361 0.004534 0.026504 -0.024494 1366 | broccoli 0.006378 0.074811 0.066825 -0.034003 0.026448 1367 | blueberries -0.052199 -0.069196 -0.079309 -0.028598 -0.060567 1368 | ""one-stop 0.027463 -0.100466 -0.037867 -0.048253 0.032788 1369 | shop"" -0.063315 0.050125 -0.064999 -0.089349 -0.099316 1370 | superfood 0.044548 -0.081917 -0.014511 0.028845 -0.046651 1371 | music -0.098601 -0.044377 0.056349 0.089423 0.027772 1372 | 2012: 0.052018 -0.019953 0.031857 -0.029424 0.089744 1373 | Third 0.009106 -0.086391 0.017018 -0.036865 -0.089424 1374 | explosives -0.021997 -0.034666 -0.073785 -0.042418 -0.066397 1375 | car -0.004833 -0.027969 0.049427 0.035321 0.018394 1376 | Olympic -0.083388 -0.054588 0.078823 -0.019253 0.085240 1377 | Facing -0.074186 0.023516 -0.042567 0.032818 0.058442 1378 | Deficit, 0.084479 -0.026120 -0.040051 0.021451 0.039365 1379 | Ariz. -0.010294 0.012577 0.022865 -0.065896 0.087658 1380 | Shifts 0.036980 -0.029846 0.012423 0.031726 0.054639 1381 | Costs 0.052090 0.066907 -0.042289 0.049063 -0.071487 1382 | Cities 0.002474 -0.033106 0.081828 -0.092782 0.009141 1383 | Media -0.021687 0.003473 -0.043940 0.096558 -0.051968 1384 | Matters: 0.098018 0.049549 -0.069389 0.025675 -0.034256 1385 | Accurately 0.005709 -0.008043 -0.045837 -0.011681 0.071424 1386 | quoting -0.013692 0.051914 0.068657 0.089660 -0.048296 1387 | Democratic 0.041011 0.083011 -0.063564 0.074898 -0.046711 1388 | Congressman 0.014211 -0.059889 0.045347 -0.022143 0.065730 1389 | constitutes -0.018940 0.063212 0.045690 -0.023369 -0.048016 1390 | 'smear' -0.096967 -0.095197 -0.062246 -0.049329 -0.034407 1391 | Hustler 0.048469 -0.101545 0.033046 -0.099364 0.089974 1392 | Fined -0.011476 0.069050 0.081756 -0.087614 0.021637 1393 | $14G 0.028992 -0.061004 0.044562 -0.033508 -0.009596 1394 | Using -0.095268 -0.083185 0.096042 -0.039813 -0.063567 1395 | Condoms 0.060768 -0.081431 -0.001332 -0.042508 0.006115 1396 | aim -0.023735 0.068194 -0.045163 -0.099697 0.082149 1397 | AARP: 0.012970 0.096375 0.065326 0.027751 0.070574 1398 | backfire? -0.087898 -0.044929 0.023796 0.027830 0.056078 1399 | passes -0.093540 0.072594 0.024109 -0.047242 0.026688 1400 | ""force 0.054197 0.049277 -0.095518 0.037803 0.083016 1401 | law"" 0.056696 -0.024156 -0.090368 -0.041591 -0.085291 1402 | dabble -0.096739 -0.077011 -0.024297 -0.060177 -0.053097 1403 | mobile 0.007698 -0.065157 0.039071 -0.089240 0.078387 1404 | payments, -0.035766 -0.006812 0.027720 0.020978 -0.070807 1405 | suggests -0.015116 -0.001745 0.004510 0.001588 -0.006364 1406 | defeats -0.097521 0.012741 -0.010483 0.049489 0.006691 1407 | 2nd -0.019270 0.040527 0.007076 -0.095151 -0.074264 1408 | Miami 0.012478 -0.070591 0.065883 -0.061626 -0.036514 1409 | Geoffrey 0.088475 0.086570 0.048040 -0.028385 -0.049769 1410 | Boycott: 0.071105 -0.050104 -0.074752 -0.062286 -0.092309 1411 | India's -0.051352 -0.002961 -0.024309 0.069779 -0.086246 1412 | victory 0.041682 0.053550 -0.094365 -0.076771 -0.024711 1413 | fitting -0.021087 0.020789 -0.087872 -0.053616 0.025198 1414 | reward 0.029311 -0.098349 0.032797 0.066124 -0.031426 1415 | superb 0.040639 0.010332 -0.058349 0.052076 0.051346 1416 | team 0.085328 -0.081290 -0.034095 0.037377 0.002845 1417 | effort -0.083139 0.062810 0.005181 0.061294 0.030367 1418 | ""superfood"": -0.017618 0.000531 -0.023206 0.029490 -0.086250 1419 | researchers 0.000389 -0.033200 -0.086057 -0.078477 -0.017499 1420 | jobless -0.002200 0.034653 -0.043133 -0.057684 0.095317 1421 | Would -0.013380 0.007572 0.018101 0.078470 -0.012001 1422 | Streamline -0.050351 -0.028417 0.051338 -0.080105 -0.017472 1423 | Quotes 0.088725 0.005147 -0.061536 -0.044761 0.022824 1424 | Idol -0.082787 0.050698 -0.034785 -0.052692 0.086666 1425 | Meter -0.044501 -0.013422 0.044729 -0.019372 0.017139 1426 | looks 0.049737 0.022558 0.037065 0.013395 -0.073396 1427 | Season -0.007148 0.019277 0.020223 -0.051937 0.070443 1428 | 10""s 0.071105 -0.012666 0.014923 -0.090536 -0.000245 1429 | ""quirkier 0.061055 0.082769 -0.038545 -0.057419 0.087746 1430 | characters"" 0.001334 -0.005037 -0.055899 0.000173 0.003354 1431 | EPA? 0.025555 -0.060546 0.020863 0.049564 -0.014062 1432 | ECZ? 0.018033 -0.040101 -0.029342 0.001995 -0.095451 1433 | Acronyms 0.008097 0.026126 0.022706 0.044626 0.061538 1434 | cause -0.067835 -0.022118 -0.038638 0.064415 -0.031025 1435 | confusion 0.084963 0.055104 -0.097820 -0.074609 -0.056680 1436 | Virus 0.094644 -0.068860 -0.066506 -0.049428 -0.031600 1437 | polar -0.061496 0.095484 0.057531 0.017128 0.087949 1438 | bear -0.031907 0.001223 0.011445 -0.067074 -0.021249 1439 | Jan 0.004411 -0.037539 -0.102944 -0.009862 0.029145 1440 | Brewer -0.070685 0.087587 0.008025 0.092206 -0.057057 1441 | Offers -0.059647 0.055635 0.040133 -0.030153 -0.007536 1442 | Restore 0.057399 0.062508 -0.024608 -0.038155 0.050393 1443 | Saving 0.070089 0.047340 0.015439 -0.084652 0.029026 1444 | Transplant 0.022957 -0.017179 0.051181 0.020924 -0.075899 1445 | Program -0.014716 0.077894 -0.085031 -0.066768 -0.063076 1446 | Exchange 0.012168 -0.036791 0.056552 0.069677 -0.013087 1447 | Taking 0.008814 0.005562 -0.076760 -0.043859 0.032567 1448 | Health 0.007241 0.044826 0.023043 -0.104536 0.072418 1449 | Care 0.092803 0.068725 -0.062781 -0.000876 0.011281 1450 | Away -0.092548 0.076832 -0.062732 -0.015894 -0.074391 1451 | 160,000 -0.096135 -0.015871 0.081652 -0.055995 0.078373 1452 | Arizonans -0.015104 -0.015145 0.062710 -0.014750 -0.087589 1453 | Avert -0.035636 -0.046403 0.099039 0.010141 0.028014 1454 | Shutdown 0.094547 0.091584 -0.075876 0.002991 0.008072 1455 | Lou 0.003301 -0.060755 -0.067836 0.034800 -0.089246 1456 | Gorman 0.021489 0.042470 0.047733 0.089946 -0.049870 1457 | 82 0.045791 0.017774 0.063040 -0.081509 0.039303 1458 | Bay 0.099105 0.060587 0.055244 0.081414 0.085482 1459 | Area's -0.009899 -0.069013 -0.035548 -0.098565 0.044449 1460 | Buck 0.002318 -0.001357 -0.080261 -0.040454 0.021190 1461 | Institute -0.002193 -0.037004 -0.095464 -0.013140 -0.003514 1462 | blazes -0.080130 -0.027952 0.087349 0.051967 -0.061988 1463 | trail -0.078063 0.064029 -0.002681 0.065244 0.037663 1464 | life -0.091678 0.092584 0.027956 -0.064975 -0.032608 1465 | span -0.045423 0.050834 -0.047618 0.018226 0.021808 1466 | extension -0.092590 0.025485 0.001268 0.093634 -0.109244 1467 | treatments -0.097166 -0.071229 0.066885 -0.008728 0.042335 1468 | scuttle -0.004299 0.054633 -0.085889 -0.059348 0.021163 1469 | Palin's -0.023662 -0.069263 -0.081603 -0.068243 -0.020245 1470 | increase 0.011468 -0.005127 0.068154 0.086690 -0.015544 1471 | Judge: 0.024940 0.041961 0.023848 -0.066427 0.069963 1472 | Ex-Somali 0.013906 0.090206 0.061626 0.003553 0.074038 1473 | questioned -0.030446 -0.039183 -0.054168 -0.052543 -0.061957 1474 | under -0.006215 -0.007306 -0.060960 -0.050618 -0.000216 1475 | oath -0.061007 -0.020627 0.017911 -0.071668 -0.088804 1476 | alleging 0.057328 -0.055213 -0.031729 -0.010079 -0.010782 1477 | rights -0.093322 0.066609 -0.092410 -0.074685 -0.093745 1478 | abuses 0.053884 0.092032 0.049508 -0.004379 0.068663 1479 | rout -0.038612 0.096686 0.094306 -0.003497 -0.025950 1480 | 111-98 -0.093269 0.044322 -0.045560 0.037471 0.038571 1481 | despite 0.061620 0.075709 0.094215 0.089010 -0.090839 1482 | missing -0.033020 -0.015235 0.081786 0.045428 -0.036924 1483 | Nash -0.005095 -0.067785 -0.079945 -0.073341 0.062572 1484 | Rotherham -0.073211 -0.068667 0.095971 -0.000992 0.040865 1485 | forward -0.037924 -0.023727 -0.095459 -0.050217 0.067626 1486 | Nico 0.098235 -0.027058 -0.045740 0.061570 0.076111 1487 | Steenkamp 0.047133 0.073830 0.041053 -0.010076 -0.103376 1488 | stimulant -0.092791 0.098301 0.070449 -0.031618 0.016027 1489 | drinking 0.094080 -0.058643 -0.086212 0.049450 0.073385 1490 | energy -0.058276 -0.087087 -0.086885 -0.002598 0.057817 1491 | supplement 0.012465 -0.001932 0.093746 0.034845 -0.051050 1492 | Michelle -0.002697 0.012018 -0.074741 -0.054152 -0.009846 1493 | Brings -0.033928 -0.011748 -0.094452 -0.044170 -0.085334 1494 | Oldies 0.039240 -0.030560 -0.002189 0.008486 -0.096825 1495 | But 0.006814 0.011092 0.062441 -0.011786 0.071171 1496 | Goodies 0.022499 -0.053489 -0.038068 0.076487 0.048293 1497 | Canine 0.050837 -0.101084 -0.090685 0.008210 -0.090114 1498 | Couture: 0.012588 0.039039 0.029889 0.080156 0.017870 1499 | Fashions -0.079209 -0.098822 -0.023575 -0.081905 -0.027694 1500 | Four-Legged -0.072338 -0.069891 -0.073782 0.039283 -0.005454 1501 | Friend -0.077055 -0.061040 0.003059 0.071200 -0.090068 1502 | Creator 0.044492 0.047974 -0.097588 -0.044365 -0.054471 1503 | Seasons 0.048491 -0.074953 0.092306 -0.050363 -0.020195 1504 | Enough -0.007971 0.005226 0.054384 -0.073753 -0.039862 1505 | Huffington -0.059011 -0.053129 -0.050417 0.011367 0.056986 1506 | Post, 0.091729 0.007082 0.016852 -0.014870 0.002154 1507 | Times -0.048478 0.051604 -0.075918 0.020560 0.058411 1508 | Jousting -0.049353 -0.083205 -0.044208 0.044545 0.069442 1509 | Paywalls, 0.044364 0.004088 -0.031069 0.086657 -0.021186 1510 | Smurfs 0.017007 0.050841 -0.078220 -0.078146 -0.057890 1511 | Outscores -0.039945 0.066320 0.013493 -0.011986 0.021450 1512 | Rallies 0.008924 -0.012593 -0.030315 0.081283 0.001090 1513 | Victory 0.086016 -0.061912 -0.092224 0.071582 0.012849 1514 | Winner 0.056583 -0.099574 0.037164 0.018372 -0.008102 1515 | Competes 0.036832 0.018474 -0.033450 -0.016940 -0.052769 1516 | God -0.047239 0.014665 -0.085044 -0.075352 -0.012653 1517 | alive, 0.055072 -0.020350 0.093283 -0.015500 -0.025193 1518 | frustrated -0.012510 -0.002390 -0.083684 0.079527 -0.047720 1519 | state -0.033885 0.012704 0.086455 -0.003848 -0.085778 1520 | religion 0.055836 0.025515 -0.084451 -0.058460 -0.040456 1521 | Lois 0.034811 0.012263 -0.067604 -0.021446 -0.080086 1522 | Lane 0.080573 -0.084809 0.079529 -0.066501 -0.031242 1523 | Kidder -0.048447 -0.062083 -0.060659 -0.011613 0.051019 1524 | Praises -0.099714 -0.094375 -0.060443 0.037147 -0.057314 1525 | Adams 0.021007 -0.011258 -0.011347 0.046231 -0.016651 1526 | Casting 0.063636 0.050830 -0.092857 0.081674 0.033657 1527 | Girlfriend 0.026115 -0.046903 0.007525 0.021779 -0.102548 1528 | helps -0.029026 -0.012064 -0.003537 -0.045958 -0.002051 1529 | Wood -0.024174 -0.005261 0.040467 0.066497 -0.043341 1530 | act 0.046385 -0.043254 0.083354 0.043970 -0.030903 1531 | When -0.000509 0.022657 0.028470 -0.102581 -0.100246 1532 | floods 0.000115 0.034300 -0.054816 0.039566 -0.029618 1533 | came: -0.008655 -0.093304 0.090675 -0.099169 0.023015 1534 | Australia -0.051923 0.049336 0.022916 -0.051445 -0.016164 1535 | Pakistan 0.092360 -0.058729 -0.098057 0.069730 0.035515 1536 | ""Obama"" 0.032719 -0.045409 -0.063412 0.016309 -0.018046 1537 | robber -0.060888 -0.044398 -0.063828 0.033120 0.086689 1538 | nabbed, -0.054579 0.093209 -0.074284 -0.007762 -0.001129 1539 | Austrian 0.088716 -0.022663 -0.000253 -0.036558 -0.028965 1540 | officials 0.086212 0.067198 0.009044 0.026728 -0.020910 1541 | v 0.063760 0.050687 -0.044447 0.018031 0.052679 1542 | Lanka: -0.049353 -0.057306 -0.084523 0.056109 -0.104834 1543 | Mahela -0.002661 -0.045945 0.080997 0.009788 0.086690 1544 | Jayawardene 0.053480 0.091500 -0.032793 -0.008045 -0.027141 1545 | century -0.089993 -0.055699 0.053441 -0.026331 -0.009305 1546 | balance -0.063626 0.075802 -0.051545 0.094183 -0.059039 1547 | Fake -0.091418 0.008364 -0.017076 0.017588 0.068122 1548 | Weed 0.067925 0.025949 0.048753 0.061625 0.071985 1549 | Claims 0.023027 -0.026942 -0.038201 0.075020 -0.006384 1550 | Another -0.026250 -0.075682 -0.031693 -0.082967 0.083379 1551 | Naval 0.024384 -0.048530 0.064057 -0.092046 -0.068124 1552 | Academy -0.040724 0.090191 0.001780 -0.041318 0.023721 1553 | Student -0.048259 -0.056259 -0.045099 -0.043803 -0.043030 1554 | Tenn. 0.080266 -0.003154 -0.084539 0.000598 -0.096261 1555 | officer -0.038700 -0.104757 0.046212 -0.100804 0.072884 1556 | responding 0.076023 0.030334 0.044265 -0.095730 0.048621 1557 | robbery 0.043119 -0.052248 -0.080914 -0.017491 0.033940 1558 | call; 0.088073 -0.050112 -0.049656 0.022615 0.072307 1559 | hospital -0.049530 -0.074567 -0.051073 -0.035189 -0.038272 1560 | Mimic -0.078743 0.048059 0.067983 -0.047574 -0.097392 1561 | ""Gang 0.037038 0.014859 -0.055403 -0.031380 -0.038992 1562 | Six"" 0.051747 0.048176 0.078351 -0.037711 -0.035639 1563 | Effort 0.020921 -0.015833 -0.082244 0.096935 -0.022280 1564 | Hand-dyed -0.067962 0.026068 -0.062155 -0.026943 -0.108296 1565 | graphic -0.097956 -0.002325 -0.001086 0.014531 0.048310 1566 | garments 0.016608 0.025652 -0.083018 0.065083 -0.086567 1567 | colour 0.064654 0.084450 -0.020625 0.093571 -0.015007 1568 | clothing -0.058088 0.059823 0.040198 -0.046698 0.052237 1569 | Anu 0.060595 0.027355 -0.049955 -0.035740 0.024159 1570 | Raina 0.055173 -0.036343 0.084273 -0.039692 0.065948 1571 | rescue -0.069219 0.029891 -0.047671 0.084538 -0.064134 1572 | help 0.027431 -0.022802 0.007575 0.083615 -0.019882 1573 | daily -0.090792 0.011278 -0.094515 -0.079429 0.073445 1574 | Zimbabwe 0.030674 0.091242 -0.059165 0.000320 -0.062141 1575 | Denise -0.017479 -0.030462 0.078359 -0.020496 0.059433 1576 | Van -0.082188 0.053894 0.056000 -0.004371 -0.036686 1577 | Outen 0.059701 0.062674 -0.018114 -0.003708 -0.033800 1578 | odd -0.011825 0.081263 -0.042765 -0.100166 0.023573 1579 | outfit 0.086405 -0.015171 0.011126 -0.041499 -0.054401 1580 | days -0.010957 -0.058670 0.054229 -0.023971 0.075667 1581 | flaunting 0.063754 -0.094569 0.029755 -0.083392 0.018456 1582 | amazing 0.011899 0.039603 -0.013790 -0.028697 -0.008052 1583 | post-baby 0.050059 -0.063086 -0.038331 0.037161 0.014206 1584 | bikini -0.078983 -0.046863 -0.105100 -0.023725 0.091055 1585 | body -0.006172 -0.059519 0.001823 0.072230 0.001024 1586 | Deputy -0.084601 -0.061912 -0.086038 0.042577 0.001507 1587 | DA -0.102036 0.088397 0.044968 -0.012881 0.046297 1588 | handled -0.059428 -0.009959 0.026186 -0.044731 -0.055327 1589 | Hilton, -0.096277 -0.007971 -0.032928 -0.034346 -0.018067 1590 | quits -0.049108 -0.081827 -0.100260 0.051972 -0.084554 1591 | cocaine -0.038560 -0.057955 0.000384 0.013027 -0.049554 1592 | Lakers' 0.044042 -0.083884 0.023267 0.043865 0.061457 1593 | Matt -0.096409 0.073095 0.082647 -0.079892 -0.010805 1594 | Barnes 0.085046 0.033804 0.019814 0.015605 0.037767 1595 | bags 0.079127 0.044321 0.064815 -0.082473 0.062021 1596 | sight -0.094968 -0.024423 -0.014158 0.041803 -0.050055 1597 | Okla. -0.002575 -0.035374 0.041756 -0.017204 0.058911 1598 | disabled 0.045048 0.029568 -0.050604 -0.091905 -0.052192 1599 | kangaroo -0.052773 -0.062201 -0.074111 -0.104190 -0.099490 1600 | therapy -0.039771 0.091634 0.091267 0.048045 -0.011823 1601 | pet 0.003804 -0.100671 -0.063204 -0.027021 -0.073242 1602 | Genetically -0.017510 0.070792 -0.016468 0.086106 -0.052213 1603 | modified 0.091300 -0.025931 0.006995 0.037990 0.078069 1604 | cows 0.014795 0.007100 -0.061640 -0.007694 -0.099589 1605 | produce -0.081978 0.065078 0.028810 -0.012900 -0.008373 1606 | ""human"" -0.028513 0.051155 -0.015035 -0.080679 0.043574 1607 | UConn’s 0.032857 -0.025083 -0.095312 0.093087 -0.018662 1608 | Maya 0.078694 0.037793 -0.078912 0.086106 0.074127 1609 | Moore 0.065850 0.039589 0.054496 -0.077621 -0.016007 1610 | wins 0.022302 0.066367 0.025507 -0.041457 0.035801 1611 | 3rd -0.024974 0.087366 0.085068 -0.034134 0.071204 1612 | Wade 0.033078 0.055871 -0.006262 -0.057060 -0.086224 1613 | Trophy -0.062913 0.015391 -0.086540 0.032685 -0.061420 1614 | Merkel -0.071036 -0.067430 -0.043935 -0.091121 -0.003907 1615 | Imbau 0.059775 -0.083739 -0.004472 0.027084 -0.084060 1616 | Myanmar 0.034177 0.059659 0.019215 0.007771 0.048190 1617 | Bebaskan -0.055377 -0.026203 -0.026652 0.052147 -0.096451 1618 | Tahanan 0.059539 -0.092895 -0.056805 -0.083463 0.072074 1619 | Politik 0.073062 -0.039294 -0.048010 0.073184 -0.098427 1620 | Ariane -0.049766 0.076492 0.051464 -0.074267 -0.000346 1621 | aborted -0.084420 -0.065035 -0.048389 0.074631 -0.097621 1622 | technical -0.058109 0.011477 0.059276 -0.009201 -0.034761 1623 | hitch -0.102317 0.094148 -0.024807 0.071345 -0.032550 1624 | ""future -0.079415 -0.089386 -0.066235 -0.101021 -0.007581 1625 | criminals"" -0.073631 -0.048787 -0.015968 -0.091244 -0.090907 1626 | Scores 0.079597 0.043544 -0.058208 0.091437 -0.068924 1627 | 3 -0.071369 -0.011163 0.022844 0.040756 0.047037 1628 | Goals 0.030150 0.019466 -0.070727 -0.004834 0.076781 1629 | United; 0.025592 -0.020341 -0.004831 -0.095694 -0.008369 1630 | Slips -0.018858 0.031727 0.056660 0.044054 0.062187 1631 | Metronomy 0.093216 0.017326 -0.072422 -0.050052 -0.024825 1632 | guide -0.067310 -0.016129 0.036153 -0.015186 0.026681 1633 | us 0.069920 0.009420 -0.051894 0.031824 -0.080339 1634 | around 0.089160 -0.104247 0.039240 0.089129 -0.036594 1635 | English 0.047003 -0.103720 0.075494 0.078903 0.032326 1636 | Riviera 0.090399 -0.017503 0.085380 -0.079187 0.022525 1637 | Dodgers' -0.034586 0.056297 -0.068546 -0.086319 -0.082208 1638 | opening-day -0.013112 -0.022362 0.089900 0.079541 -0.073403 1639 | payroll 0.075555 -0.077122 -0.089098 -0.089746 0.008542 1640 | increases -0.065220 0.001792 0.089755 -0.061577 0.012164 1641 | Brooke -0.090170 -0.056432 0.016128 -0.061202 0.067695 1642 | Shields -0.048882 -0.085494 -0.100201 -0.022958 -0.019090 1643 | Broadway 0.070328 -0.042911 -0.016104 -0.018351 0.030693 1644 | Addams -0.051791 0.058766 0.027892 0.073709 -0.092918 1645 | Family"" -0.022173 -0.056401 -0.046720 -0.016281 0.077639 1646 | Celebrity 0.030686 -0.025287 0.081210 -0.084293 0.049585 1647 | Circuit 0.083993 0.028945 -0.047718 -0.013756 0.067186 1648 | Unimportance 0.026417 0.027302 0.034059 -0.052922 -0.104150 1649 | Apps -0.003592 -0.006566 -0.063568 -0.063625 0.040861 1650 | Blocks 0.063627 0.097319 0.013478 -0.037610 0.081226 1651 | Health-Care 0.081480 0.007571 -0.013779 -0.025844 0.000105 1652 | Implementation 0.012816 0.030476 -0.013109 -0.087699 0.042419 1653 | Ripples -0.092345 0.010281 -0.026197 0.055116 -0.049129 1654 | Saturn 0.079357 0.002561 0.064581 -0.053796 0.068206 1655 | Jupiter 0.037005 0.038166 0.023854 -0.087620 -0.057288 1656 | rings -0.065993 0.062860 0.056699 0.066487 -0.039095 1657 | ""caused 0.058559 -0.052680 -0.095432 -0.061203 -0.101371 1658 | comet 0.066754 -0.068783 -0.052154 0.056513 -0.105402 1659 | crashes"" 0.068244 -0.012600 0.093523 -0.035743 -0.053019 1660 | Booby-trap -0.007266 0.039113 0.011701 -0.050046 0.011388 1661 | Northern -0.045204 0.073833 -0.083802 0.004778 0.074283 1662 | Shopping 0.063685 -0.094620 -0.078275 0.012237 0.021797 1663 | Bargains: 0.022565 -0.078763 -0.055678 0.031538 -0.066345 1664 | Out -0.077025 -0.019075 -0.037014 -0.064289 -0.110306 1665 | These 0.050866 -0.098204 0.058167 0.003545 0.084283 1666 | Great -0.023813 -0.077634 -0.072658 0.014788 0.076477 1667 | Deals -0.082294 0.066765 -0.049979 -0.041278 0.023720 1668 | Coupon -0.077823 0.061352 -0.105033 0.021306 -0.093365 1669 | Codes 0.067986 0.008212 -0.074461 -0.002812 -0.084550 1670 | Lace-Making 0.096411 -0.098675 -0.030681 -0.024043 -0.054674 1671 | Consumer -0.041637 -0.029563 0.089901 0.065458 0.028162 1672 | Shoe 0.080249 0.060828 0.044259 0.040889 0.090650 1673 | Index -0.050594 -0.083961 -0.030855 0.054792 0.027380 1674 | Spanish -0.071623 0.030344 -0.024311 -0.048530 -0.036315 1675 | jailbird -0.069324 0.028019 -0.022760 0.018936 -0.093388 1676 | sly 0.031608 -0.030428 0.030024 -0.052816 0.018165 1677 | fax 0.064404 -0.087939 -0.034806 -0.029298 -0.074734 1678 | Orleans -0.080881 -0.039474 0.034836 -0.091847 0.081965 1679 | post-Katrina -0.039409 -0.099579 -0.035653 -0.007828 0.018631 1680 | shooting -0.043549 0.004035 -0.001719 0.006690 0.060959 1681 | burning 0.079718 -0.039428 -0.051825 -0.028976 0.059796 1682 | Henry -0.058017 0.008917 0.054161 0.068763 -0.076291 1683 | Glover 0.075278 -0.062799 0.091912 0.022332 0.012134 1684 | Michigan 0.091012 0.070999 0.027519 -0.072138 -0.083029 1685 | beating -0.070794 -0.079457 0.026442 -0.080630 -0.094750 1686 | 73-year-old 0.015998 0.083176 0.018133 0.037482 0.058678 1687 | crossing -0.069997 0.041221 -0.006846 -0.100563 -0.010040 1688 | end 0.095213 -0.022861 0.039186 -0.041491 -0.083604 1689 | AIDS -0.010676 -0.074959 0.081672 0.000692 0.075455 1690 | sets -0.099298 -0.064396 -0.007332 -0.033434 0.047005 1691 | zero -0.011782 -0.078657 -0.045450 0.013614 0.034086 1692 | infections 0.044140 -0.098856 -0.102648 -0.092964 0.050949 1693 | Clouds 0.069807 -0.051129 0.020731 0.086293 -0.017160 1694 | Amazon.com -0.081529 -0.050002 0.004418 -0.049549 0.081022 1695 | Silk 0.077355 -0.008082 -0.030691 -0.053020 0.056573 1696 | shirts 0.023238 -0.045102 -0.018796 -0.006638 -0.028097 1697 | immune 0.001314 -0.020499 0.040990 0.070504 0.069744 1698 | recession 0.098723 -0.036000 0.024682 0.008039 -0.010084 1699 | soar 0.046959 -0.073438 -0.013052 -0.081318 -0.006652 1700 | approves -0.029905 0.089184 0.066871 -0.032152 0.092062 1701 | town 0.093590 -0.031731 -0.102840 0.089756 0.035650 1702 | caps -0.093222 0.000716 -0.019217 0.088091 -0.072176 1703 | Benefits -0.047459 0.051222 0.016920 -0.088804 0.086750 1704 | Therapy -0.053574 -0.008461 -0.029886 0.046462 -0.025074 1705 | Outweigh -0.062888 -0.069118 0.057444 -0.061398 -0.007787 1706 | Risks -0.018417 -0.019494 0.092380 0.031412 -0.107718 1707 | Second 0.077711 0.064168 -0.097401 0.093395 -0.101300 1708 | Cancer: -0.095005 -0.047439 -0.067727 0.015371 0.031548 1709 | 16 0.002975 0.045925 0.033075 0.039347 0.081834 1710 | Agusan -0.059461 0.066739 -0.027939 -0.010673 0.024270 1711 | teachers, -0.033706 0.060123 0.033394 -0.076499 -0.004224 1712 | kidnapped -0.010541 -0.089404 0.086870 -0.082610 0.053361 1713 | Job -0.011784 -0.016336 -0.069421 -0.035456 0.054779 1714 | growth -0.053166 -0.096591 0.033623 0.049786 -0.075004 1715 | outlook -0.014031 0.087449 0.017914 -0.051847 0.082239 1716 | Franco 0.018887 0.013275 0.059538 -0.011715 0.065792 1717 | deletes 0.067804 -0.102791 -0.035989 -0.007770 -0.098681 1718 | account 0.019665 0.038271 -0.082482 -0.048848 -0.017588 1719 | Best 0.016524 -0.101669 0.007610 0.054648 -0.104415 1720 | friend -0.040970 -0.071995 -0.053380 0.096918 0.082951 1721 | pot 0.018814 0.077477 0.012458 0.020350 -0.067362 1722 | house -0.092122 -0.077320 0.065463 -0.029319 0.034004 1723 | 1-Bank -0.094049 -0.069971 -0.053368 -0.094791 0.037840 1724 | America -0.012097 -0.075834 -0.068538 0.004125 0.013863 1725 | Q1 -0.077791 -0.028028 -0.030480 0.025218 0.014750 1726 | muni 0.084041 0.027649 0.016129 -0.045122 -0.000638 1727 | underwriter; -0.083700 -0.014835 0.021281 -0.097156 -0.104675 1728 | total 0.023224 0.056284 -0.086934 -0.001594 -0.009048 1729 | girl 0.077348 -0.104901 -0.058868 0.070112 -0.111452 1730 | recalls 0.062470 0.014068 -0.091910 -0.073762 -0.007914 1731 | run-in 0.099065 0.053427 0.085333 0.070693 -0.010984 1732 | Philip 0.081014 -0.028599 -0.051855 0.061366 0.041100 1733 | Markoff 0.073324 -0.078099 -0.005625 -0.082326 0.030318 1734 | Chic -0.049936 0.008968 0.000619 0.084801 -0.022440 1735 | Dear -0.072957 0.064071 0.019830 0.008323 -0.083039 1736 | … -0.084182 -0.037608 -0.040186 -0.039316 -0.063496 1737 | Syria's 0.036451 -0.102153 0.003263 0.020075 -0.015518 1738 | Leader -0.090810 0.029315 0.024368 0.004332 -0.080576 1739 | Defiant -0.079060 0.014876 -0.008910 0.079198 0.068899 1740 | Speech -0.058143 0.044488 -0.063535 -0.039776 -0.031354 1741 | Since -0.050943 0.004081 -0.100384 -0.101696 0.049774 1742 | Protests -0.010508 -0.016159 -0.066222 -0.094169 -0.023500 1743 | Rivers 0.030640 0.061192 -0.076125 -0.020422 -0.070456 1744 | promises 0.041608 0.090400 -0.066756 -0.034776 0.010590 1745 | rib 0.037335 -0.010280 0.066362 0.055880 0.004577 1746 | Wilkins -0.084120 0.052208 0.046060 0.030767 -0.084387 1747 | dust-up -0.037089 0.010752 -0.098659 0.055966 -0.012606 1748 | ex-ref 0.005651 -0.005819 -0.066188 -0.046182 0.011831 1749 | Next 0.041673 -0.001732 0.069955 0.087826 0.008841 1750 | 3D 0.084996 0.017181 0.014659 0.013596 -0.003295 1751 | Camera? 0.066999 0.026081 -0.014624 -0.046575 0.000997 1752 | Final -0.034642 -0.025346 -0.019735 0.007841 0.042623 1753 | Four 0.089292 0.041318 -0.022745 0.089574 -0.075842 1754 | berths, 0.072039 0.057868 0.029131 -0.038695 -0.064522 1755 | Butler, 0.053375 0.002089 0.013430 -0.086242 -0.041080 1756 | VCU -0.084947 -0.078274 0.052220 0.018008 0.048733 1757 | Cinderellas? 0.059906 -0.009608 -0.047382 0.016387 0.044151 1758 | hiatus 0.073807 -0.101779 -0.059577 -0.074340 -0.035166 1759 | Can 0.045679 0.093597 0.079177 -0.077398 -0.035048 1760 | painkillers 0.047153 -0.001953 0.008631 -0.007257 0.053057 1761 | prevent 0.043986 0.027442 -0.040339 -0.044557 0.076267 1762 | melanoma? 0.045267 0.053035 0.069153 0.096786 0.060424 1763 | unemployment 0.027960 0.072054 0.057027 -0.045306 -0.000404 1764 | low, 0.021563 0.048285 0.055257 -0.026419 0.010948 1765 | labor 0.094147 -0.037625 -0.026817 0.081148 -0.062535 1766 | participation 0.055738 0.043945 -0.016979 0.066745 0.023381 1767 | Get 0.039346 -0.076540 -0.078383 0.043235 0.058556 1768 | Ground 0.031814 -0.032461 0.080906 0.005458 0.030784 1769 | Floor 0.066782 -0.021118 -0.087899 -0.026580 0.051861 1770 | Plastics 0.061048 0.038603 0.013923 0.022953 0.027977 1771 | Kraton 0.075630 -0.082494 -0.079816 0.069126 -0.089532 1772 | Undersea -0.080324 0.085954 -0.084437 -0.026722 0.010846 1773 | volcanoes -0.045315 -0.074510 0.047322 0.028621 -0.090239 1774 | don't 0.009809 0.084664 -0.048107 -0.047331 -0.053521 1775 | just 0.066774 0.085602 -0.043550 0.041646 -0.067297 1776 | ooze, -0.101876 0.022127 0.019124 -0.010644 0.019378 1777 | they 0.033906 0.043912 -0.099807 0.087027 -0.030606 1778 | also -0.065811 0.000758 -0.038343 0.001171 0.030809 1779 | explode 0.007696 -0.074741 0.065732 0.048778 -0.055727 1780 | fashion -0.021682 -0.056191 0.074260 0.079385 0.086884 1781 | week -0.077802 -0.078858 0.080909 -0.083166 -0.068766 1782 | Native -0.020296 0.054985 -0.046058 -0.096134 0.052318 1783 | Searching 0.044205 0.027492 -0.037933 -0.011306 0.040303 1784 | alien -0.086284 0.073391 -0.034871 -0.091044 -0.027292 1785 | life? -0.044604 0.023591 0.090383 -0.031261 -0.098740 1786 | failed 0.005628 -0.063052 0.082137 0.008375 -0.088084 1787 | Sean -0.067679 -0.100854 -0.077185 0.029863 -0.067091 1788 | Parnell -0.085059 -0.083304 -0.058772 0.087119 0.046357 1789 | Appointee -0.019650 -0.061185 -0.073441 0.037067 -0.017661 1790 | Who 0.075934 -0.113391 -0.102788 0.052817 -0.010048 1791 | Wants 0.066545 0.019697 0.053367 -0.016693 0.004683 1792 | Criminalize -0.076303 0.066762 -0.011056 0.092992 -0.036310 1793 | Sex 0.023394 0.038009 0.063454 0.047327 -0.049439 1794 | Outside -0.004455 0.000435 0.017217 0.043448 -0.085297 1795 | Marriage 0.046971 0.027324 -0.051757 -0.023246 0.079315 1796 | Faces 0.023957 -0.078044 -0.045871 0.040132 0.013565 1797 | Pushback -0.042422 -0.053366 -0.090982 0.014868 0.049177 1798 | Coast: -0.009670 0.057601 -0.026708 -0.077762 0.049942 1799 | Fighting -0.041666 -0.002822 -0.061397 -0.052341 -0.041892 1800 | rages -0.072621 0.036132 -0.072619 0.091389 0.011876 1801 | president's -0.043672 0.002526 -0.103553 0.046220 -0.009589 1802 | palace -0.062519 0.022038 -0.034636 -0.079129 -0.023766 1803 | Tree -0.049243 0.034682 -0.048936 -0.029256 -0.061080 1804 | premiere 0.014587 0.043576 0.056303 -0.016577 0.015110 1805 | UK 0.053047 0.017503 0.027490 -0.021044 0.042164 1806 | gov't -0.081622 0.019129 0.008767 -0.086004 -0.027594 1807 | turns 0.015132 -0.026263 -0.019537 0.077507 -0.055313 1808 | ceasefire -0.011097 -0.037174 0.032759 -0.030784 -0.079320 1809 | offer -0.047366 -0.072984 0.077156 0.014923 -0.086745 1810 | rebels -0.076405 0.007683 -0.047100 0.030454 -0.000493 1811 | everywhere 0.069889 0.018747 0.017153 -0.047833 -0.081534 1812 | bakeries -0.026329 -0.048071 0.056770 0.037541 -0.055058 1813 | vending 0.002450 -0.035947 0.005403 0.037661 -0.049614 1814 | machines 0.015722 -0.060668 0.076155 0.042879 0.051043 1815 | (but -0.008937 -0.074111 -0.075477 0.051415 0.020966 1816 | worry, 0.061600 -0.111778 -0.002248 -0.090845 -0.042442 1817 | you -0.080600 -0.017401 0.062662 -0.044509 0.072380 1818 | eat 0.025576 -0.065616 -0.042166 -0.017647 0.071052 1819 | cinema 0.082520 -0.078820 0.052453 -0.097645 -0.077524 1820 | guilt 0.005089 0.037840 -0.097384 -0.101572 -0.022450 1821 | free) 0.009282 -0.057142 -0.030022 -0.056578 -0.013240 1822 | Connection 0.052266 0.055299 0.018568 -0.009648 0.052513 1823 | condemns 0.034768 0.013109 -0.001446 -0.103229 0.053846 1824 | crackdown, -0.063339 -0.104447 -0.053269 -0.101138 0.058545 1825 | presses -0.051354 -0.038645 -0.059284 -0.063303 0.075581 1826 | reform -0.011549 -0.045688 0.056281 -0.020722 0.052951 1827 | coral 0.061880 -0.070846 0.011894 0.041926 -0.008748 1828 | well 0.009621 0.018160 -0.043906 0.052178 0.037013 1829 | Doubts 0.079911 -0.083052 -0.062588 0.001887 0.014972 1830 | follow -0.077605 0.034485 0.058150 0.094134 0.061062 1831 | Five 0.034389 -0.043094 -0.015402 0.043332 -0.048624 1832 | Stories -0.064901 0.017584 -0.069510 -0.048752 -0.100312 1833 | We -0.037444 -0.042553 -0.073727 -0.082631 -0.057606 1834 | Wish 0.083998 -0.104982 0.035934 -0.081786 -0.004513 1835 | Were 0.052879 -0.033308 0.090199 0.036092 0.020919 1836 | Jokes -0.083195 0.079452 0.055782 -0.003664 0.090153 1837 | Concierge -0.092385 0.026476 -0.043861 -0.061384 0.082847 1838 | fills -0.037167 0.038380 -0.028083 0.016887 0.064479 1839 | niche, 0.006788 -0.057781 -0.008568 0.069132 -0.089274 1840 | its -0.036786 0.059521 -0.063560 0.058170 -0.097249 1841 | spread -0.006588 -0.050385 0.032326 -0.054629 -0.026377 1842 | worsen -0.019199 0.067647 -0.089351 -0.079074 -0.063936 1843 | frontline 0.046878 -0.071762 -0.091795 0.000924 -0.029304 1844 | doctors -0.099418 0.032305 -0.051703 0.091823 0.049877 1845 | Rebels -0.099526 -0.076975 -0.097676 0.071279 -0.084391 1846 | Seize -0.062290 -0.032535 -0.056439 -0.044843 -0.087506 1847 | TV, 0.067193 0.031170 0.055680 -0.097204 -0.079023 1848 | Control -0.071091 0.016059 0.075567 -0.071286 -0.093462 1849 | Gbagbo 0.068306 0.016720 0.007700 0.044009 -0.105524 1850 | NHL 0.049847 0.064070 0.014431 -0.091507 -0.040364 1851 | game 0.037263 -0.034756 0.086507 -0.066803 -0.002195 1852 | results, -0.020101 0.066044 0.084849 0.090146 -0.002807 1853 | 1 0.000528 0.020315 -0.067749 -0.001029 -0.093272 1854 | champ -0.024420 0.083355 -0.010059 -0.065757 -0.039815 1855 | Texas 0.016180 0.075067 -0.058960 0.093549 -0.028944 1856 | rallies 0.054380 0.090552 -0.017193 0.038046 -0.105044 1857 | 9-5 0.065895 -0.052895 -0.046611 0.014840 -0.072053 1858 | Mossad's -0.020688 -0.089738 0.054360 0.039465 0.067180 1859 | secret -0.036519 0.015296 0.040976 -0.009287 0.040607 1860 | Potbellied 0.047986 -0.033767 -0.008038 -0.081720 0.047650 1861 | men 0.004712 0.042700 0.014188 0.032138 0.026736 1862 | go -0.097316 0.076379 -0.067083 -0.060019 -0.097777 1863 | blind 0.086712 -0.105358 0.031814 -0.029910 0.058531 1864 | Charla 0.067513 -0.086868 0.047049 0.054393 0.082799 1865 | Nash, 0.089376 0.080106 0.074204 -0.036770 0.004328 1866 | mauled 0.040338 -0.030792 0.026962 -0.093511 -0.008919 1867 | chimp, 0.086445 -0.104035 0.006466 -0.047753 0.075480 1868 | waiting -0.099803 0.011869 -0.064397 0.001351 -0.003617 1869 | transplant -0.091061 -0.079715 -0.087219 0.027223 -0.025507 1870 | NIreland -0.002455 -0.043660 -0.088528 -0.072050 -0.087647 1871 | acquitted -0.032712 -0.016566 -0.018720 -0.030606 0.014821 1872 | 1977 0.006651 -0.010614 0.057193 0.084758 0.074229 1873 | soldier 0.082608 -0.063219 -0.068069 -0.044577 -0.038124 1874 | Correction: -0.088757 0.016705 -0.007139 0.071104 -0.102877 1875 | Ohio 0.010002 -0.069833 -0.086497 0.080712 0.035343 1876 | Union 0.082890 -0.033174 -0.060522 -0.068248 -0.010971 1877 | Fight -0.049229 -0.106216 0.042403 -0.068213 -0.082274 1878 | story -0.021909 0.070992 0.072837 0.088371 0.067021 1879 | counting -0.086127 0.095168 -0.077362 -0.031197 0.007217 1880 | exempt -0.077509 0.019243 -0.042151 0.080757 -0.068559 1881 | movie -0.068574 0.047572 -0.005892 -0.099485 -0.062024 1882 | $9 0.053088 -0.038019 -0.090656 0.002583 -0.094271 1883 | bequest 0.001880 -0.008805 -0.055096 0.071513 0.034263 1884 | offers 0.034127 -0.029282 0.029705 0.000005 -0.048445 1885 | ‘stability’ -0.071982 -0.044045 0.006897 0.038965 -0.019964 1886 | seek -0.083930 -0.036081 -0.017584 0.066385 -0.100587 1887 | cut -0.011979 -0.038879 -0.087522 -0.062627 -0.049696 1888 | bailout 0.094805 0.001982 0.061582 -0.042661 0.008804 1889 | next -0.001194 -0.017903 -0.055916 0.007072 0.077127 1890 | week: 0.052666 0.081303 0.058495 0.031391 -0.103957 1891 | Sweet! 0.046919 0.080021 -0.037418 -0.021291 -0.032203 1892 | Candy -0.092534 -0.061377 0.067450 -0.055195 0.020560 1893 | eaters 0.058162 0.069404 -0.102063 0.078810 -0.087606 1894 | surprisingly 0.031686 0.078331 -0.101478 0.051139 -0.066364 1895 | slimmer 0.076059 -0.040023 0.022812 -0.097653 0.015776 1896 | Skewed 0.050626 0.062583 0.083417 -0.050798 0.032820 1897 | priorities -0.023351 -0.040131 0.038835 0.066085 0.041643 1898 | urges 0.063662 -0.033533 -0.060660 -0.041095 0.006707 1899 | gay, 0.051615 -0.023389 -0.077289 0.052611 -0.065118 1900 | transgender -0.011999 -0.018026 0.053427 -0.007024 -0.077962 1901 | UberSocial -0.037001 0.025084 -0.045297 -0.008707 0.022338 1902 | fixes -0.080822 0.067868 0.008939 -0.075263 0.059235 1903 | privacy 0.100194 0.080704 0.017730 -0.016540 -0.090942 1904 | bug -0.052711 0.071693 -0.002167 0.052796 -0.099773 1905 | South -0.070862 -0.007993 -0.095458 -0.098053 0.061336 1906 | Pole's 0.012008 -0.086332 0.033784 -0.036012 0.047707 1907 | Building -0.068115 -0.065596 0.034817 0.080289 -0.016564 1908 | Blown -0.020413 -0.015031 0.046610 -0.066534 0.081099 1909 | Up 0.092152 0.089640 0.008056 -0.045447 0.017261 1910 | Fukushima -0.050128 -0.059799 0.067404 -0.044396 -0.095503 1911 | 'Bones' 0.005009 -0.090748 -0.090453 -0.086037 -0.084958 1912 | Star -0.074904 0.033148 -0.098549 -0.046839 0.049625 1913 | Emily -0.099810 0.002749 0.076163 0.058759 -0.088135 1914 | Deschanel -0.090816 0.003080 -0.045546 0.091301 0.049672 1915 | pregnant 0.058089 -0.086976 0.039312 0.060105 -0.085778 1916 | first -0.068445 0.062627 -0.014650 0.001436 -0.008276 1917 | Jon -0.067846 0.084040 0.050885 0.069356 0.071293 1918 | Stewart -0.048185 -0.010839 0.063923 -0.094268 -0.012957 1919 | fan -0.068711 -0.017736 -0.033718 0.065883 -0.073182 1920 | Obama's 0.034220 -0.019964 -0.092202 -0.019966 0.066723 1921 | Libya -0.091817 -0.019891 0.028431 0.043533 -0.003926 1922 | Slovenian 0.078061 -0.026387 0.035220 -0.049621 0.005803 1923 | undergoes 0.043986 0.027929 0.084677 0.029215 0.000719 1924 | prostate 0.067671 -0.039867 0.036383 0.027117 0.021032 1925 | tumour 0.010461 -0.044761 0.032446 -0.093997 0.039664 1926 | Violet 0.021756 0.088206 0.007826 0.033922 -0.084666 1927 | Lace: 0.016242 -0.081848 0.018194 0.074537 -0.050687 1928 | Cruz, 0.087216 0.016019 -0.025058 0.071157 -0.013659 1929 | Vanessa -0.013258 -0.044407 0.054841 -0.102126 0.030237 1930 | Hudgens, -0.029620 0.005211 0.046571 0.030811 -0.000708 1931 | Karolina -0.023436 -0.095224 -0.067820 -0.028956 0.014159 1932 | Kurkova 0.053231 0.088487 -0.088144 -0.008369 -0.037577 1933 | MYLEENE -0.010835 -0.024887 -0.041666 0.059393 -0.041001 1934 | KLASS -0.023642 -0.111193 0.051954 0.054193 0.027820 1935 | SHOWS -0.103566 0.058153 0.008438 -0.053196 -0.039249 1936 | OFF 0.021011 0.070904 -0.029396 0.062775 -0.070391 1937 | HER 0.089594 -0.087806 -0.007956 -0.074319 -0.012982 1938 | NEWBORN -0.067350 -0.086435 -0.044513 -0.064357 0.065263 1939 | HERO -0.092430 -0.030943 -0.073024 -0.098277 -0.071616 1940 | ""Westside 0.084768 -0.083628 -0.085356 -0.012497 -0.078064 1941 | Rapist"" -0.016290 -0.042445 -0.073255 -0.005323 0.075282 1942 | killings 0.016446 -0.004598 -0.054370 -0.073067 -0.043419 1943 | ‘Miral’: -0.087244 0.068705 -0.073502 0.049426 -0.100882 1944 | Director -0.012338 0.072309 -0.084886 -0.041373 -0.096882 1945 | has 0.030050 -0.087217 0.012437 -0.074928 -0.072717 1946 | conflict 0.074141 0.084844 0.067041 0.013095 -0.077455 1947 | interest 0.021797 0.049978 0.017150 0.067233 0.020402 1948 | Plus 0.060304 -0.097228 -0.031667 0.093807 -0.094090 1949 | size -0.072358 -0.021952 0.034309 0.019945 0.012918 1950 | fashion: 0.055536 -0.062144 0.081919 0.037793 0.050121 1951 | Size 0.000606 -0.011548 -0.019333 -0.023848 0.077384 1952 | clothes -0.064289 0.068537 -0.027885 -0.033455 -0.076721 1953 | CAN 0.035892 0.009160 0.019195 0.086063 -0.043053 1954 | look -0.058934 -0.083569 -0.034618 -0.092856 -0.015430 1955 | stylish -0.091182 -0.064741 -0.085848 -0.102611 0.056306 1956 | ""Snow-bate"" -0.052883 0.085391 -0.032623 -0.101067 -0.077396 1957 | lands 0.068155 -0.082067 0.087322 0.047611 0.023094 1958 | ""Lumpy"" 0.029833 -0.030386 0.045586 0.077756 -0.072957 1959 | Minnesota -0.062505 0.006329 0.044950 0.075730 0.011746 1960 | events -0.041021 -0.018366 0.043414 0.047411 -0.036791 1961 | Amazing -0.096345 0.085692 -0.000440 0.016934 0.037816 1962 | Disappearing 0.016384 0.025856 0.089543 -0.050310 -0.054471 1963 | Neutrino -0.088618 -0.018490 0.062834 -0.056108 0.036350 1964 | Geoid: -0.007533 -0.036295 0.065051 -0.023772 -0.098112 1965 | map 0.095923 0.016587 0.042969 -0.094873 0.071892 1966 | Earth's -0.087634 -0.055467 -0.023787 -0.011822 -0.098085 1967 | gravity -0.082833 -0.051732 -0.061171 -0.056165 -0.048987 1968 | yields 0.087423 0.043204 -0.049703 -0.063305 -0.020916 1969 | potato-shaped -0.054282 -0.036921 0.031204 0.028162 0.073729 1970 | planet -0.073451 0.023046 -0.028132 -0.045654 -0.017552 1971 | Cameron 0.076663 0.069876 -0.030657 0.027090 0.013557 1972 | Future 0.034060 0.033077 0.067252 -0.068947 0.063141 1973 | Cinema 0.049410 -0.063438 0.029484 -0.015015 0.061685 1974 | Cablevision 0.086994 0.038244 -0.003410 -0.025316 0.010233 1975 | Optimum -0.077238 0.007664 0.087418 -0.017220 0.069559 1976 | available, -0.024172 -0.055482 -0.059386 0.006282 0.083324 1977 | streams 0.062693 0.027396 -0.031982 0.087841 0.036694 1978 | hundreds 0.046042 -0.047182 0.004495 -0.044582 -0.012301 1979 | channels -0.058780 -0.053182 0.009586 0.003602 -0.111497 1980 | plus 0.095060 -0.081819 -0.087007 0.016557 -0.024942 1981 | VOD 0.003970 0.009711 -0.001560 -0.103932 -0.035456 1982 | display 0.042453 0.057399 -0.088447 0.088759 -0.094690 1983 | desire -0.062576 -0.097460 -0.000196 0.091151 0.018139 1984 | German -0.060758 -0.055379 -0.064289 0.094323 0.049986 1985 | stadium 0.091147 0.089129 -0.012358 -0.096701 0.003367 1986 | plot -0.056840 -0.051917 0.018432 0.093228 -0.019687 1987 | Schneider -0.014251 -0.072337 0.001705 -0.104488 0.069652 1988 | Electric 0.061925 -0.095177 -0.028467 0.049625 -0.075836 1989 | Reaches 0.076082 0.020794 -0.035985 0.072863 0.014785 1990 | Soitec 0.024316 0.039888 -0.051093 0.073318 -0.014920 1991 | Solar -0.081200 -0.011045 -0.083206 -0.001783 -0.013571 1992 | Power 0.077786 0.002833 0.068370 -0.011351 -0.046051 1993 | Venture -0.058354 -0.034786 -0.056209 -0.074750 0.032133 1994 | enter -0.068392 0.081164 0.085222 0.045976 -0.066725 1995 | race -0.021490 0.083143 0.036674 0.073130 -0.029835 1996 | £8m -0.057115 -0.098845 -0.097263 -0.064219 0.030352 1997 | QPR -0.053598 -0.065859 -0.034286 -0.059418 -0.052410 1998 | playmaker 0.000730 -0.055934 0.087797 -0.076925 0.037127 1999 | Taarabt -0.066111 0.057314 -0.036591 -0.022608 -0.075552 2000 | Accidentally -0.001317 0.017755 -0.085208 0.078478 -0.052413 2001 | Secret 0.032529 0.085165 -0.029267 -0.018949 -0.061505 2002 | Details -0.083471 0.036315 0.037721 -0.093785 0.049639 2003 | Singer -0.064137 -0.043313 0.089266 -0.091339 -0.078793 2004 | Michael -0.021763 -0.083756 -0.102320 0.063779 -0.104814 2005 | Buble -0.091099 -0.016025 -0.059190 -0.012105 -0.027617 2006 | weds -0.043987 0.068361 0.080178 -0.002942 -0.100179 2007 | Luisana 0.063216 -0.022211 -0.050221 -0.021049 -0.100948 2008 | Lopilato 0.073038 -0.080207 0.017823 -0.017635 -0.074869 2009 | Gunmen -0.090466 -0.051096 -0.000067 0.063873 0.016886 2010 | south -0.092762 0.045604 0.074232 0.091253 -0.085296 2011 | Philippines 0.072593 -0.045947 0.041138 -0.094650 0.017584 2012 | kidnap 0.014819 0.072813 -0.089549 0.068604 -0.101093 2013 | 16, 0.055768 0.010178 0.087271 0.083583 -0.072961 2014 | demands -0.091034 -0.030742 -0.055592 -0.099598 -0.054733 2015 | Curious 0.055855 -0.075981 -0.049501 0.070500 0.057530 2016 | About 0.048329 0.005365 -0.017524 0.087870 0.044578 2017 | Georges -0.040293 0.027341 0.071318 0.098639 0.043390 2018 | Hands-free -0.055319 -0.041866 0.055413 0.067345 0.029769 2019 | faucet 0.010697 0.022178 0.062167 0.038282 0.081020 2020 | official -0.062910 -0.051565 -0.063870 0.071269 -0.045006 2021 | highly 0.075099 -0.088969 -0.079763 0.043035 -0.064523 2022 | leaking 0.032940 -0.075620 0.088856 -0.060539 -0.016670 2023 | ocean -0.077546 0.073996 -0.083736 -0.032022 0.031659 2024 | Omagh -0.076579 -0.011288 -0.026145 0.073050 0.089029 2025 | ""a 0.067418 -0.085383 0.084585 0.093938 0.034045 2026 | double 0.004346 0.071423 0.068453 -0.052256 -0.078703 2027 | insult"" -0.029437 0.005684 -0.014190 -0.083100 0.034236 2028 | agenda: -0.082488 -0.044201 -0.043156 -0.065614 -0.105539 2029 | EcoTools -0.094782 -0.082158 0.059154 -0.031473 0.061713 2030 | e-commerce, 0.005572 -0.039261 0.050622 -0.008037 0.059411 2031 | Karl 0.011392 0.069419 -0.032034 -0.086624 -0.062383 2032 | Lagerfeld 0.038741 -0.087416 -0.063222 0.053482 -0.087829 2033 | opens 0.021055 -0.095955 -0.009630 0.002200 0.017108 2034 | beauty 0.049276 -0.071229 -0.040642 -0.076051 -0.038029 2035 | pop-up -0.075655 -0.059752 -0.057388 0.020195 -0.077391 2036 | Hyundai 0.031240 -0.010132 0.036957 0.089502 -0.104683 2037 | Off -0.089881 -0.085517 -0.050103 0.027774 -0.018336 2038 | Blue 0.093297 0.026993 -0.003477 -0.097554 -0.066685 2039 | Fuel 0.009416 0.020777 -0.040641 -0.023034 -0.062792 2040 | Cell -0.077879 -0.030816 0.055699 -0.001819 -0.091818 2041 | Concept, -0.096961 -0.026702 -0.022124 -0.104999 -0.042259 2042 | Two -0.017127 -0.122092 -0.094788 -0.016833 0.055301 2043 | Hybrid 0.036830 -0.054332 0.069757 -0.027537 -0.087011 2044 | Models 0.061214 -0.099998 -0.024518 0.090090 0.001092 2045 | H&M -0.003058 -0.040149 -0.052247 -0.012281 -0.086731 2046 | Profit -0.007712 -0.029206 -0.082446 -0.060145 0.068171 2047 | Misses 0.006753 0.015017 0.061493 0.076368 -0.047200 2048 | Estimates 0.059148 -0.111331 -0.077220 -0.014022 0.059340 2049 | Retailer 0.046021 0.042339 0.084977 -0.084422 0.019190 2050 | Absorbs -0.077994 -0.057632 0.029151 0.020274 -0.061247 2051 | Cost -0.100985 -0.050325 -0.032154 -0.035425 -0.069824 2052 | Rises 0.023779 0.063005 -0.089790 0.063979 0.064086 2053 | Now, 0.056958 -0.018489 0.028626 0.069479 0.046033 2054 | get -0.073446 -0.051322 0.001443 0.045258 0.091863 2055 | shape 0.045504 -0.038313 -0.009392 0.045530 0.059208 2056 | swimsuit 0.025410 -0.004516 0.008872 0.060994 -0.017774 2057 | acts 0.030760 -0.075770 -0.027495 0.094354 -0.079547 2058 | slimsuit! -0.080893 0.079765 0.083973 0.061674 0.012648 2059 | Morning-after 0.043444 0.069165 0.078162 -0.094396 -0.007616 2060 | pill 0.039754 -0.042181 -0.065140 -0.029893 -0.100224 2061 | free 0.028928 -0.047100 0.044019 -0.039065 -0.008547 2062 | teenage -0.035120 0.058994 -0.039960 -0.073412 0.059091 2063 | Wales -0.013828 -0.015901 -0.017920 -0.069471 -0.072040 2064 | Andrew -0.043391 -0.023475 -0.056619 0.069566 -0.069145 2065 | interviewed -0.077220 -0.086997 0.038271 -0.040873 -0.049822 2066 | again 0.013524 0.035908 0.029212 0.060452 -0.031205 2067 | Edwards 0.095920 0.077864 0.084220 0.064255 -0.061458 2068 | Knollenberg -0.062508 -0.063329 -0.102231 0.093764 0.048099 2069 | challenges 0.012448 -0.013505 -0.059061 0.078027 0.024574 2070 | Peters 0.008865 0.024004 -0.044086 -0.044906 -0.002267 2071 | WRAPUP -0.037795 0.021008 0.057792 -0.000536 0.070011 2072 | 3-Fitch 0.015532 0.081946 0.026990 0.085166 -0.097337 2073 | needs -0.103430 0.026748 -0.033401 0.047495 -0.017942 2074 | bailout, -0.093748 0.088258 0.042230 0.080921 -0.090948 2075 | S&P -0.018740 -0.102169 -0.091328 0.078114 0.020260 2076 | cuts 0.000897 -0.092829 0.031702 -0.053730 0.016946 2077 | Emissions 0.007228 -0.024377 0.070510 -0.071568 -0.104596 2078 | Cap 0.093297 0.022762 0.075825 0.061937 -0.111456 2079 | Trade -0.031796 -0.085547 0.066341 0.042841 0.000452 2080 | Created -0.045865 -0.069131 0.063789 -0.055307 0.027751 2081 | Toxic -0.081132 0.081229 0.015957 -0.012960 -0.053109 2082 | Hotspots? -0.030067 -0.016812 -0.056081 -0.083783 0.054653 2083 | Syrian 0.083297 -0.082176 0.092077 -0.079322 0.024041 2084 | form 0.083602 -0.023211 -0.049246 -0.035942 -0.089639 2085 | government -0.003217 0.041557 0.051295 0.067540 0.054601 2086 | Phish 0.033335 -0.011747 -0.012955 0.064539 -0.005794 2087 | Phavors 0.010113 0.083456 0.066334 -0.068389 0.001498 2088 | 3-Day -0.070090 0.071038 -0.074951 0.062208 0.082496 2089 | Phestival 0.054914 -0.030042 -0.025344 0.030806 -0.086879 2090 | Phinger -0.037901 0.057894 0.055652 -0.061493 -0.089388 2091 | Lakes 0.075419 -0.059576 -0.029206 -0.061861 -0.103529 2092 | prank -0.010976 0.041266 -0.076021 -0.101365 0.024376 2093 | 2011: 0.029687 0.034090 -0.077857 -0.027776 0.083907 2094 | Heinrich -0.036875 0.037207 0.056878 -0.025060 -0.060380 2095 | run 0.044506 -0.078875 -0.034009 0.071672 0.056029 2096 | ME's 0.050076 0.046175 0.063432 0.030904 -0.069392 2097 | corpse 0.077282 -0.033186 -0.015663 -0.064874 -0.002152 2098 | grab 0.041414 -0.093809 -0.042152 0.038424 -0.097676 2099 | George 0.090987 -0.107557 0.064288 0.026039 -0.112679 2100 | Wong -0.004186 0.069532 0.032284 -0.081506 -0.057504 2101 | 'unforgivable' -0.044694 0.042993 0.021774 -0.105260 0.019712 2102 | friend, -0.043921 0.023512 0.032503 -0.067544 0.066809 2103 | Bobby -0.081343 0.060340 -0.036222 -0.099302 -0.041853 2104 | Heedles -0.044607 -0.023830 -0.108156 0.041797 -0.091022 2105 | 'Dark -0.034272 0.020112 -0.025132 -0.083255 -0.113667 2106 | Shadows' 0.003622 0.074897 -0.017056 0.086227 -0.102186 2107 | update: 0.060742 -0.003287 0.035906 -0.103632 0.042685 2108 | Moretz 0.077544 -0.089977 -0.039090 -0.052375 -0.099425 2109 | join 0.002223 0.013743 -0.031363 0.056826 -0.096549 2110 | cast -0.071356 0.026984 0.008654 0.045298 -0.042573 2111 | Popping -0.061266 -0.031202 -0.059305 0.008446 0.063611 2112 | calories 0.025109 0.014389 0.093067 -0.046308 -0.025942 2113 | DVD 0.000801 -0.070346 0.064610 0.070622 -0.066650 2114 | Blu-ray -0.075665 -0.009450 -0.066116 0.032129 -0.008774 2115 | tribes 0.011121 -0.100633 -0.051653 0.021166 -0.009364 2116 | boy -0.102661 0.065613 0.009159 0.032344 -0.121090 2117 | among 0.056841 0.076774 0.020100 -0.001946 -0.114904 2118 | burrito -0.076265 0.083911 -0.082743 0.051549 -0.107432 2119 | stand -0.070802 0.045831 0.044455 -0.000256 0.073710 2120 | Sweden's 0.015072 -0.030753 -0.095459 -0.062447 -0.078409 2121 | suing -0.013154 0.057185 0.077127 -0.103604 -0.084212 2122 | Chinese -0.090590 -0.095823 -0.079922 0.047576 0.086946 2123 | ZTE -0.095950 -0.089559 -0.073224 -0.063300 0.046356 2124 | Public, -0.081892 -0.002910 -0.097303 -0.029299 -0.093343 2125 | Victims -0.023960 0.042069 0.079442 -0.079724 -0.068204 2126 | Abuse 0.050725 -0.055491 0.004412 -0.089089 0.015414 2127 | Home 0.074036 -0.041614 0.074283 -0.094483 -0.045453 2128 | deem-and-pass -0.039150 -0.066556 -0.020523 -0.032767 0.062229 2129 | without -0.002947 -0.034409 0.072296 0.055050 0.038472 2130 | Time 0.066693 -0.036024 0.044725 0.041129 -0.040268 2131 | Warner 0.058342 -0.086036 -0.077570 -0.012260 -0.079720 2132 | Cable 0.055495 -0.089543 -0.002402 0.052618 -0.041641 2133 | channel -0.077186 -0.103382 -0.022628 0.077107 -0.065000 2134 | lineup 0.077516 0.086746 -0.009371 -0.001569 -0.010605 2135 | ""In -0.087021 0.007861 0.001602 0.006603 -0.008506 2136 | Better -0.004099 0.039326 0.068418 -0.095289 -0.045795 2137 | World"" -0.014163 0.078407 0.084526 -0.004342 0.008753 2138 | Broncos 0.072823 -0.083126 -0.059669 0.009185 -0.056038 2139 | brass -0.022221 0.061554 -0.067911 -0.040588 -0.033955 2140 | skip 0.084950 -0.004982 -0.047957 -0.047686 0.011574 2141 | D-lineman -0.095435 0.049503 0.055841 -0.042432 0.011448 2142 | Bowers 0.018374 -0.061231 -0.018195 0.068638 -0.055351 2143 | Dozens 0.013661 -0.085168 -0.049001 0.008537 -0.110736 2144 | Injured 0.004260 0.076297 0.015389 -0.077197 0.041188 2145 | 50-Car 0.041032 0.028090 0.013375 -0.002340 0.033004 2146 | Crash -0.077414 -0.083600 0.001673 -0.071953 -0.014023 2147 | Abu 0.009749 0.062310 -0.055427 0.069263 0.049646 2148 | Dhabi 0.002521 0.017456 -0.058393 -0.097808 0.035396 2149 | boars -0.047917 -0.087871 0.022089 0.084705 -0.003037 2150 | Germany 0.046393 0.029540 -0.106840 -0.020469 0.046100 2151 | legacy -0.081299 0.092978 -0.052132 -0.056148 0.066075 2152 | Chernobyl 0.042838 0.079836 0.009520 0.088054 0.030339 2153 | Three 0.057226 0.043805 -0.077862 -0.067301 0.050500 2154 | airstrike -0.072822 -0.081463 -0.013555 -0.009780 0.034416 2155 | Nicky 0.060203 -0.020258 0.014073 -0.042156 0.036729 2156 | figure -0.063354 -0.021496 -0.102932 -0.049692 -0.129252 2157 | daring 0.072245 -0.049322 0.084300 -0.005393 -0.071299 2158 | lace 0.032871 0.037499 -0.012604 -0.031708 -0.110085 2159 | panel -0.007927 0.016956 -0.018630 0.018057 0.037882 2160 | vest -0.021707 0.028248 0.063312 -0.043649 -0.118309 2161 | slashed 0.003323 -0.091003 -0.063974 -0.087844 -0.034810 2162 | denim -0.046485 0.041597 0.005211 -0.083571 -0.126313 2163 | shorts -0.065266 0.054160 -0.031417 -0.081328 -0.034532 2164 | sells 0.032008 -0.070325 0.028941 -0.096172 0.037031 2165 | Delphi -0.061858 0.046775 -0.092381 0.021304 -0.006554 2166 | $3.8B -0.048540 -0.021215 0.079396 0.033471 0.074011 2167 | Dior -0.072315 0.063545 -0.006842 -0.098258 -0.118337 2168 | Replace 0.020440 0.027466 -0.108544 -0.085077 -0.097696 2169 | Galliano -0.080387 -0.013001 -0.089255 -0.062747 -0.092719 2170 | Whenever 0.003140 0.030401 0.028188 -0.039091 -0.040021 2171 | They 0.013282 -0.072766 -0.045820 -0.031629 0.027624 2172 | Darn -0.068357 -0.036930 0.086620 0.009534 0.052016 2173 | Well -0.070081 0.084665 -0.077528 -0.059196 0.026117 2174 | Please, -0.071682 -0.039976 0.016591 0.089571 0.050297 2175 | All 0.038200 -0.045512 0.012966 0.042088 0.016593 2176 | Right? 0.070056 0.020298 0.041290 0.044002 -0.029616 2177 | Apr. -0.043117 -0.019666 -0.080008 -0.079046 -0.058966 2178 | 1, -0.047216 -0.079060 0.052448 0.011824 -0.035871 2179 | Theaters: -0.024293 -0.060709 0.079539 0.047220 -0.092647 2180 | Rainn 0.007707 -0.004628 0.041027 -0.089919 0.042317 2181 | Wilson -0.105236 0.063601 0.055089 -0.103932 -0.067097 2182 | Ellen -0.002394 -0.061547 -0.067854 -0.101352 -0.117727 2183 | "Super" -0.050329 -0.019417 -0.047803 -0.096220 -0.021071 2184 | heroes 0.056245 0.020551 0.087651 -0.063313 -0.100250 2185 | Fashion 0.027011 -0.032900 0.031058 -0.005749 0.037722 2186 | life: -0.082861 0.080219 0.086291 -0.024420 -0.049647 2187 | ethics -0.105050 -0.081455 -0.104509 -0.002733 -0.104562 2188 | style 0.011018 0.036606 0.028316 -0.097904 -0.103628 2189 | Grit 0.055572 0.037011 -0.016151 0.054816 -0.023417 2190 | Hailee -0.004153 0.056354 -0.006501 -0.069925 0.074377 2191 | Steinfeld 0.093145 -0.026518 -0.000475 -0.038500 -0.101779 2192 | Sleeping -0.039560 0.058155 0.029499 0.075571 0.053435 2193 | Dim 0.056842 0.016548 -0.085233 -0.043112 0.085413 2194 | Bulb: -0.068133 -0.053179 0.078728 -0.005490 -0.002997 2195 | Sen. 0.092073 -0.102208 -0.033231 0.084462 0.008485 2196 | Barbara 0.094832 -0.030993 -0.070255 0.083648 -0.010908 2197 | Boxer, -0.018396 0.032529 0.060528 -0.067477 -0.022575 2198 | D-Calif. -0.072822 0.085499 0.074963 -0.099074 0.030054 2199 | Corrected: 0.025430 -0.085585 -0.061940 -0.087751 0.028066 2200 | Blvd 0.011870 -0.047057 -0.100159 -0.007947 0.039975 2201 | Calipari 0.040478 0.040580 0.022874 -0.019204 -0.070530 2202 | sticking -0.060084 -0.042289 0.004251 -0.007315 -0.016280 2203 | one-and-done 0.044802 0.024663 0.092191 -0.093690 0.007615 2204 | approach 0.071299 0.034958 0.052614 0.028068 0.071124 2205 | Vodafone -0.027004 0.032996 -0.070000 -0.008507 -0.046975 2206 | Essar -0.010440 0.093263 0.014905 0.054898 -0.052476 2207 | 33% 0.045490 -0.035588 -0.003152 -0.019432 -0.042212 2208 | Entrenched 0.032837 -0.078693 -0.044294 0.018727 0.010060 2209 | resistance 0.046223 -0.061925 -0.013610 -0.017053 0.053373 2210 | country -0.071818 0.021343 -0.004295 -0.057438 0.002842 2211 | Nashville -0.053260 0.035657 -0.087738 0.043567 -0.014805 2212 | Week 0.044123 -0.084993 -0.047465 -0.011555 -0.092773 2213 | Ahead: 0.030303 0.075865 0.045575 -0.047104 0.018489 2214 | Stocks, -0.000065 -0.072086 -0.027505 0.053035 0.081684 2215 | Market 0.008375 0.047301 -0.051413 0.034778 -0.071954 2216 | Tibet -0.037664 -0.043329 -0.015098 0.058338 -0.022412 2217 | through 0.020269 -0.086824 -0.038070 -0.063703 -0.059615 2218 | Lenses-Potala 0.011130 0.077191 -0.041344 -0.058423 -0.045516 2219 | Game 0.076668 -0.096785 -0.082390 0.063605 -0.007823 2220 | / -0.020812 0.062092 0.054979 -0.102136 0.036111 2221 | 5, -0.089540 0.044578 0.056438 -0.018214 -0.054390 2222 | Astros 0.072272 -0.013908 0.022963 -0.065467 0.005542 2223 | comments 0.041141 0.028436 -0.008717 0.077295 0.007745 2224 | prompt 0.068077 -0.041157 0.069780 0.040199 -0.068895 2225 | remove -0.095767 0.038814 0.009976 0.034844 -0.086924 2226 | children 0.035699 0.033717 0.060291 0.066784 -0.066090 2227 | teacher's 0.067501 0.039898 -0.090726 0.029551 0.039550 2228 | class -0.009380 -0.018710 -0.019967 0.075021 0.010323 2229 | limit 0.067967 0.024867 0.040424 0.076462 0.073865 2230 | carriers' -0.005384 0.060481 -0.046988 0.044362 -0.017969 2231 | flexibility. -0.097651 0.035406 -0.015066 -0.028342 -0.039855 2232 | Good. 0.010965 -0.031178 -0.038196 -0.040831 -0.089771 2233 | Car 0.038578 -0.071542 0.063726 -0.056372 0.073691 2234 | N. 0.050037 -0.032455 -0.019911 -0.017617 0.078116 2235 | Everton -0.020627 -0.094300 -0.079866 0.091996 -0.112468 2236 | 2-2 -0.090887 0.055086 0.089634 0.034556 -0.082721 2237 | Aston -0.064429 -0.039807 -0.100317 -0.061025 -0.076077 2238 | Villa: -0.069875 -0.032432 0.019515 -0.032217 -0.048352 2239 | Leighton 0.037167 0.080036 -0.055888 -0.046464 0.026065 2240 | Baines 0.081347 -0.001631 -0.024505 0.022899 -0.127297 2241 | saves -0.042553 -0.095105 -0.079349 0.021040 0.063381 2242 | Toffees 0.088537 0.024498 -0.042255 -0.094186 -0.071867 2243 | brace 0.035737 0.029364 0.044994 -0.079120 -0.091731 2244 | Darren 0.092287 -0.109141 0.074659 0.082326 0.000426 2245 | Bent -0.003610 0.063786 0.043121 0.068690 -0.064178 2246 | Prostate -0.051571 -0.005856 -0.088414 0.071210 -0.031872 2247 | Screening -0.037489 -0.038953 -0.026502 0.049367 -0.098141 2248 | Cut -0.084376 0.084239 -0.084050 -0.093469 -0.003386 2249 | Death 0.062161 -0.020151 -0.030313 -0.078628 0.034171 2250 | Rates: -0.060001 -0.035753 -0.008267 0.016315 -0.004024 2251 | Japanese -0.002133 0.059285 0.075667 -0.085525 -0.049637 2252 | dog: 0.075580 -0.063089 -0.057148 0.012876 -0.090844 2253 | survival -0.084503 0.060806 -0.074884 0.064769 -0.077920 2254 | saga -0.055466 -0.002062 -0.020643 -0.097973 0.079402 2255 | identify 0.050067 0.018121 -0.068687 -0.034070 -0.025783 2256 | pursuit -0.078454 0.013124 0.045937 0.009584 -0.037864 2257 | creator 0.083037 -0.061265 -0.056817 0.076640 -0.073291 2258 | glad 0.020027 -0.029013 -0.049849 -0.097339 -0.061633 2259 | Family 0.009813 -0.105328 -0.072854 -0.010623 0.072566 2260 | died 0.053974 0.061434 0.080050 0.054472 -0.106342 2261 | contaminated -0.085087 -0.063763 0.004723 0.053114 0.021579 2262 | IV 0.017427 0.038519 0.014137 0.067946 -0.053028 2263 | sues -0.033863 -0.054242 -0.040641 -0.073698 -0.069079 2264 | Free 0.054045 -0.021435 -0.022561 -0.093821 0.025832 2265 | registry -0.033019 -0.085139 -0.099372 -0.046072 -0.056400 2266 | replaces 0.053303 -0.033591 -0.011225 -0.072668 -0.054177 2267 | fishing 0.067737 -0.050997 -0.025777 0.049799 0.066423 2268 | licenses 0.010770 -0.006189 -0.089140 0.008536 0.002493 2269 | Winners 0.086292 0.052392 -0.080036 0.005701 0.021659 2270 | Buzz 0.011291 -0.056533 0.025764 -0.085121 0.021224 2271 | Suit: 0.001806 -0.099429 -0.000745 -0.064451 -0.009950 2272 | ACLU -0.053269 0.062388 -0.051752 -0.064402 0.005824 2273 | YMCA, 0.006476 0.073771 -0.110106 -0.076822 -0.130128 2274 | Privacy -0.061158 0.051916 -0.039836 -0.023517 0.043595 2275 | on? -0.052570 0.069540 -0.062168 -0.069835 -0.064342 2276 | Miley -0.099671 0.049575 0.040761 -0.103183 -0.086030 2277 | Cyrus -0.010411 -0.046976 -0.025436 -0.045910 -0.076713 2278 | Liam 0.006643 -0.066985 0.009343 0.083738 -0.062184 2279 | Hemsworth -0.076087 0.049658 0.068613 -0.080253 0.037622 2280 | been -0.042403 0.035193 0.050557 -0.023861 -0.132115 2281 | 'hanging 0.047489 0.009388 -0.090150 0.009852 0.055954 2282 | lot' 0.024104 0.023986 0.062796 -0.057112 -0.077883 2283 | five -0.062238 -0.106399 -0.093156 -0.092816 -0.085838 2284 | months 0.068490 -0.016896 -0.052161 -0.013509 -0.086857 2285 | split 0.069389 -0.074972 -0.056078 -0.049736 -0.089818 2286 | Head 0.089614 0.018303 -0.057401 -0.022498 0.007212 2287 | Latest 0.092189 -0.091298 -0.096896 -0.094483 0.003425 2288 | Showdown 0.057408 -0.020314 0.004516 -0.060461 -0.035886 2289 | Royal 0.007205 -0.109215 -0.010407 -0.024751 0.041969 2290 | bridesmaid 0.002813 -0.092953 -0.070629 -0.002597 -0.017769 2291 | Pippa 0.011035 -0.050539 -0.009139 -0.102090 -0.025588 2292 | Middleton 0.028792 -0.059572 -0.094965 -0.059566 -0.011094 2293 | kissed -0.080242 0.053863 0.050583 -0.029495 0.046246 2294 | goodbye 0.078238 0.001359 0.056602 -0.072704 0.017812 2295 | secrecy -0.020111 0.026996 -0.012697 -0.046556 -0.075107 2296 | designer -0.005474 0.081395 0.063093 0.036590 -0.087809 2297 | Hot 0.037326 0.081724 -0.045386 -0.033533 -0.103654 2298 | Pants -0.045109 -0.099803 0.087469 -0.074306 -0.055550 2299 | Maria -0.092208 -0.009556 0.082515 -0.045788 0.043204 2300 | beaten 0.021449 0.036896 -0.020976 0.076216 0.028828 2301 | Open 0.064994 -0.058756 0.013366 -0.064519 0.062151 2302 | inspired 0.006668 -0.005043 -0.016251 -0.003087 -0.052477 2303 | Victoria 0.075080 0.005607 0.031113 -0.084055 -0.061086 2304 | Milla -0.022869 -0.055969 0.029215 -0.018917 -0.114639 2305 | Jovovich 0.050115 -0.037531 0.057194 -0.114764 -0.075553 2306 | unflattering -0.076435 -0.007406 0.084908 -0.034582 0.017447 2307 | high-waisted -0.015942 -0.083254 -0.008233 0.001618 -0.045797 2308 | jeans -0.050897 0.039112 -0.041794 -0.037705 0.005111 2309 | enters 0.042584 0.056399 0.024685 -0.023401 -0.075899 2310 | mighty 0.012600 0.089515 -0.014551 0.040679 -0.053774 2311 | Chewbacca! 0.008470 0.002004 -0.072674 0.060186 0.025437 2312 | Click -0.006111 -0.027362 0.037903 -0.111014 -0.097808 2313 | Bag -0.061957 0.019805 -0.044096 0.089818 -0.018393 2314 | Magazines 0.052480 -0.001347 -0.065342 0.067904 0.040810 2315 | Become -0.036784 0.064318 -0.099260 -0.014183 -0.063238 2316 | E-Tailers -0.091652 0.059906 0.012310 -0.048902 0.014973 2317 | Stylish 0.041785 -0.070539 -0.010424 -0.044525 -0.028007 2318 | rainwear -0.035242 0.081564 -0.085262 0.002326 0.064061 2319 | whole 0.067115 -0.063123 -0.087125 -0.012034 -0.117421 2320 | family -0.045222 -0.073148 -0.108137 0.045543 -0.069582 2321 | Early 0.071687 -0.096649 -0.041393 -0.086875 -0.021326 2322 | Line -0.024975 -0.006802 0.009257 -0.005833 -0.026179 2323 | CenturyLink 0.084237 -0.015643 -0.088487 -0.098824 0.055743 2324 | completes 0.096211 0.072610 -0.075519 -0.001035 -0.040143 2325 | acquisition -0.033657 -0.081288 -0.015559 0.044323 -0.061751 2326 | Trunk -0.026418 -0.061302 0.025526 -0.018800 -0.057023 2327 | Franco, -0.006755 0.072058 -0.098189 -0.049228 0.009987 2328 | earnest -0.097681 -0.097272 0.044131 -0.086987 -0.061780 2329 | D.C., 0.028852 -0.069376 0.006077 -0.001378 -0.028609 2330 | quitting 0.001557 -0.034946 -0.111410 0.012641 -0.038141 2331 | Twitter, 0.025709 0.040165 -0.074540 -0.108282 -0.129289 2332 | PhD -0.004199 -0.007578 -0.063793 0.053729 -0.025276 2333 | cemetery -0.083919 0.090537 0.004854 -0.059014 0.080515 2334 | Undercover 0.005576 -0.033534 0.074058 -0.070365 -0.046081 2335 | Audio 0.079273 0.048411 0.076645 -0.048709 -0.012779 2336 | Rajaratnam's 0.066020 -0.049627 -0.039968 -0.075486 -0.096718 2337 | Frantic 0.035843 0.068240 0.070580 0.002644 -0.110150 2338 | Calls -0.034890 0.065878 -0.027014 0.056512 0.042154 2339 | Goldman-Buffett -0.081726 -0.100020 0.029241 -0.001763 0.026596 2340 | Tip -0.021433 0.031408 -0.001318 0.086280 -0.071564 2341 | ""Glee"" 0.077172 0.069204 0.069396 0.008185 -0.081557 2342 | Matthew 0.030533 0.010446 0.067238 -0.029990 -0.021593 2343 | Morrison 0.075178 -0.087050 -0.049141 0.089000 -0.045873 2344 | announces -0.084240 -0.006638 -0.069456 0.005564 0.014851 2345 | Gillard -0.016845 -0.075035 -0.085327 -0.050298 -0.044907 2346 | wakes -0.053497 -0.103343 -0.032069 -0.007820 0.026959 2347 | long-term -0.069921 -0.034570 0.054742 0.070329 -0.038922 2348 | threat -0.073774 -0.039593 0.013954 0.013869 0.040428 2349 | Fast 0.010404 0.085751 -0.010707 -0.095390 0.001653 2350 | Site? -0.102116 0.011938 0.077904 -0.032341 -0.039201 2351 | It -0.016856 -0.003817 0.008920 -0.034185 -0.115354 2352 | Google’s -0.020121 -0.105537 0.053387 -0.076790 -0.106117 2353 | Speed -0.021555 -0.081934 0.000232 0.078174 0.060483 2354 | Online -0.026054 -0.101941 0.037540 -0.016556 -0.110419 2355 | Launching -0.017358 0.082810 0.092598 0.036283 -0.015709 2356 | Multi-Pronged 0.050283 -0.050958 -0.101560 0.032272 -0.068226 2357 | Attack 0.047843 -0.055818 -0.031773 0.068374 -0.057880 2358 | Senior 0.077697 0.053263 -0.103653 -0.108649 -0.106035 2359 | Citizens 0.054518 -0.039730 0.049479 0.054528 -0.009940 2360 | Democracy -0.019948 -0.009806 0.090435 -0.015929 0.048370 2361 | Dividends -0.017445 -0.063141 -0.096539 0.022609 0.000707 2362 | vogue -0.015453 -0.085315 0.086651 0.061167 -0.010488 2363 | Wall 0.080949 0.089716 -0.065456 -0.053591 -0.065099 2364 | Street -0.001196 -0.074085 0.006618 0.063340 -0.021113 2365 | Craig 0.093332 -0.007191 0.072018 -0.097608 0.074169 2366 | ""will 0.059111 0.046771 -0.079308 0.004892 0.040888 2367 | do -0.032197 -0.001920 0.084936 -0.092226 0.022331 2368 | Larsson 0.029848 -0.019604 0.053917 0.022420 -0.101195 2369 | justice"" -0.072020 0.033033 0.009267 -0.018478 0.027378 2370 | Where's -0.010640 0.016733 0.033536 0.023458 -0.007419 2371 | bump? -0.090614 -0.080036 -0.039377 0.016270 -0.054795 2372 | Upon -0.098658 -0.003985 -0.020779 -0.114491 0.040895 2373 | exit, -0.082110 -0.073635 -0.003905 -0.109728 0.066132 2374 | Nassau's 0.087218 -0.059291 0.016136 0.007869 0.010295 2375 | Mulvey -0.077167 -0.093846 0.024816 0.006603 -0.011761 2376 | tips 0.005249 -0.042634 0.019262 -0.053355 -0.070073 2377 | hat -0.070248 -0.056664 -0.114470 -0.091553 -0.050823 2378 | Mangano -0.045707 -0.022143 -0.017179 -0.018827 0.058834 2379 | winegrowers 0.033229 0.022176 -0.063999 0.066235 -0.114339 2380 | shrug 0.041902 -0.064564 -0.015430 -0.082193 -0.121180 2381 | pride, -0.049055 -0.101362 -0.032573 0.059349 -0.056885 2382 | bottle -0.077861 -0.060138 0.066883 -0.052482 -0.081019 2383 | plastic -0.071884 -0.047572 -0.047208 -0.038738 -0.094271 2384 | Tech 0.036390 -0.068687 0.085228 0.051557 -0.062831 2385 | Land: -0.003859 -0.051967 0.014309 0.043987 -0.066297 2386 | Comic 0.011554 -0.057793 -0.044314 -0.096278 0.012979 2387 | Sans -0.009921 -0.006739 0.093694 -0.002474 -0.003037 2388 | Ten -0.049982 -0.001422 0.089763 0.019549 -0.011586 2389 | trapped -0.008375 -0.094944 -0.048034 -0.100120 0.024262 2390 | underground -0.054672 0.048661 0.038200 -0.053844 -0.121646 2391 | coal 0.028477 -0.106387 0.080057 -0.096794 -0.056122 2392 | mine 0.074926 -0.056132 -0.039920 0.029136 -0.068249 2393 | accident 0.027341 0.071611 0.031153 -0.081544 -0.017260 2394 | Xinjiang 0.012738 -0.107647 -0.076238 -0.041230 -0.004131 2395 | Barnes' -0.023141 -0.010092 0.085373 0.006719 0.054674 2396 | humiliating 0.028997 -0.016329 0.019403 -0.061065 -0.002277 2397 | hairstyles -0.026401 0.050670 -0.047431 0.079303 -0.038517 2398 | Nasdaq, 0.046550 -0.109174 0.073486 0.016351 -0.013422 2399 | ICE -0.039181 0.063650 -0.026386 0.058738 -0.001561 2400 | bid 0.001971 -0.010808 0.069055 -0.034226 -0.101356 2401 | snatch -0.063478 0.010752 0.013031 -0.055327 -0.115388 2402 | NYSE -0.030359 -0.113749 0.039179 -0.020561 0.049504 2403 | Germans 0.006402 -0.101776 -0.041895 -0.035859 0.071816 2404 | Feds -0.040603 0.046976 -0.078497 -0.055202 0.074926 2405 | Investigate -0.043500 -0.054805 -0.031248 -0.096938 -0.002575 2406 | Seattle 0.032812 -0.080413 -0.105401 0.081426 -0.018264 2407 | Possible 0.066810 -0.079604 0.048034 0.003516 -0.066224 2408 | Excessive -0.009101 -0.004732 -0.036261 -0.083630 -0.138529 2409 | Use 0.089459 0.012141 -0.009490 -0.019569 -0.028533 2410 | Chris 0.005970 0.053623 -0.103868 -0.018349 -0.057834 2411 | Christie -0.058890 0.059218 0.076854 -0.038270 -0.104826 2412 | Strong -0.054294 0.024189 0.074526 -0.047550 0.042331 2413 | Presidential 0.028117 -0.095158 0.075746 0.043520 -0.010057 2414 | Contender: -0.030117 -0.087958 -0.065728 0.024545 -0.085744 2415 | Poll 0.083952 0.066276 0.063592 -0.088308 -0.100731 2416 | LizaMoon -0.070239 -0.035954 -0.001622 -0.005467 0.043997 2417 | infects 0.085661 -0.031742 0.002927 0.040683 0.053923 2418 | millions -0.025440 -0.032365 0.067602 0.019023 0.013839 2419 | websites -0.016355 0.080211 0.003363 -0.072228 -0.034472 2420 | Italy -0.018877 -0.009882 0.075097 0.058799 0.018045 2421 | Create 0.020583 -0.066394 0.023821 0.085332 0.029445 2422 | Investment -0.083049 0.080559 -0.005220 -0.058425 0.029514 2423 | Vehicle -0.040099 0.048226 -0.051866 -0.000973 -0.123925 2424 | soon-report -0.041489 -0.071569 -0.029509 -0.075279 -0.038686 2425 | -------------------------------------------------------------------------------- /Preprocess.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | import numpy as np 3 | import tensorflow as tf 4 | import cPickle, os, collections 5 | import Config 6 | 7 | config = Config.Config() 8 | config.vocab_size += 4 9 | 10 | def Read_WordVec(config): 11 | with open(config.vec_file, 'r') as fvec: 12 | wordLS = [] 13 | vec_ls =[] 14 | fvec.readline() 15 | 16 | wordLS.append(u'PAD') 17 | vec_ls.append([0]*config.word_embedding_size) 18 | wordLS.append(u'START') 19 | vec_ls.append([0]*config.word_embedding_size) 20 | wordLS.append(u'END') 21 | vec_ls.append([0]*config.word_embedding_size) 22 | wordLS.append(u'UNK') 23 | vec_ls.append([0]*config.word_embedding_size) 24 | for line in fvec: 25 | line = line.split() 26 | try: 27 | word = line[0].decode('utf-8') 28 | vec = [float(i) for i in line[1:]] 29 | assert len(vec) == config.word_embedding_size 30 | wordLS.append(word) 31 | vec_ls.append(vec) 32 | except: 33 | print line[0] 34 | assert len(wordLS) == config.vocab_size 35 | word_vec = np.array(vec_ls, dtype=np.float32) 36 | 37 | cPickle.dump(word_vec, open('word_vec.pkl','w'), protocol=cPickle.HIGHEST_PROTOCOL) 38 | cPickle.dump(wordLS, open('word_voc.pkl','w'), protocol=cPickle.HIGHEST_PROTOCOL) 39 | 40 | return wordLS, word_vec 41 | 42 | def Create_Keyword_Voc(config): 43 | kwd_ls = [] 44 | with open(os.path.join(config.data_dir, 'TrainingData_Keywords.txt'), 'r') as fr: 45 | for line in fr: 46 | kwd = line.decode('utf-8').split() 47 | kwd_ls += kwd 48 | 49 | c = collections.Counter(kwd_ls) 50 | 51 | kwd_voc = [] 52 | for word in c: 53 | if c[word] >= config.keyword_min_count: 54 | kwd_voc.append(word) 55 | 56 | print 'size of keyword vocabulary:', len(kwd_voc) 57 | cPickle.dump(kwd_voc, open('kwd_voc.pkl','w'), protocol=cPickle.HIGHEST_PROTOCOL) 58 | 59 | return kwd_voc 60 | 61 | def Read_Data(config, kwd_voc): 62 | trainingdata = [] 63 | with open(os.path.join(config.data_dir, 'TrainingData_Text.txt'),'r') as ftext, open(os.path.join(config.data_dir, 'TrainingData_Keywords.txt'),'r') as fkwd: 64 | for line1, line2 in zip(ftext, fkwd): 65 | line1 = line1.decode('utf-8') 66 | doc = line1.split() 67 | line2 = line2.decode('utf-8') 68 | keywords = [word for word in line2.split() if word in kwd_voc] 69 | 70 | trainingdata.append((doc, keywords)) 71 | return trainingdata 72 | 73 | print 'loading the trainingdata...' 74 | DATADIR = config.data_dir 75 | vocab, _ = Read_WordVec(config) 76 | key_word_voc = Create_Keyword_Voc(config) 77 | data = Read_Data(config, key_word_voc) 78 | 79 | word_to_idx = { ch:i for i,ch in enumerate(vocab) } 80 | idx_to_word = { i:ch for i,ch in enumerate(vocab) } 81 | data_size, _vocab_size = len(data), len(vocab) 82 | 83 | print 'data has %d document, size of word vocabular: %d.' % (data_size, _vocab_size) 84 | 85 | keyword_voc_size = len(key_word_voc) 86 | keyword_to_idx = { ch:i for i,ch in enumerate(key_word_voc) } 87 | 88 | def data_iterator(trainingdata, batch_size, num_steps): 89 | epoch_size = len(trainingdata) // batch_size 90 | for i in range(epoch_size): 91 | batch_data = trainingdata[i*batch_size:(i+1)*batch_size] 92 | raw_data = [] 93 | key_words = [] 94 | for it in batch_data: 95 | raw_data.append(it[0]) 96 | tmp = np.zeros(keyword_voc_size) 97 | for wd in it[1]: 98 | tmp[keyword_to_idx[wd]] = 1.0 99 | key_words.append(tmp) 100 | 101 | data = np.zeros((len(raw_data), num_steps+1), dtype=np.int64) 102 | for i in range(len(raw_data)): 103 | doc = raw_data[i] 104 | tmp = [1] 105 | for wd in doc: 106 | if wd in vocab: 107 | tmp.append(word_to_idx[wd]) 108 | else: 109 | tmp.append(3) 110 | tmp.append(2) 111 | tmp = np.array(tmp, dtype=np.int64) 112 | _size = tmp.shape[0] 113 | data[i][:_size] = tmp 114 | 115 | key_words = np.array(key_words, dtype=np.float32) 116 | 117 | x = data[:, 0:num_steps] 118 | y = data[:, 1:] 119 | mask = np.float32(x != 0) 120 | yield (x, y, mask, key_words) 121 | 122 | 123 | train_data = data 124 | writer = tf.python_io.TFRecordWriter("sclstm_data") 125 | dataLS = [] 126 | for step, (x, y, mask, key_words) in enumerate(data_iterator(train_data, config.batch_size, config.num_steps)): 127 | example = tf.train.Example( 128 | # Example contains a Features proto object 129 | features=tf.train.Features( 130 | # Features contains a map of string to Feature proto objects 131 | feature={ 132 | # A Feature contains one of either a int64_list, 133 | # float_list, or bytes_list 134 | 'input_data': tf.train.Feature( 135 | int64_list=tf.train.Int64List(value=x.reshape(-1).astype("int64"))), 136 | 'target': tf.train.Feature( 137 | int64_list=tf.train.Int64List(value=y.reshape(-1).astype("int64"))), 138 | 'mask': tf.train.Feature( 139 | float_list=tf.train.FloatList(value=mask.reshape(-1).astype("float"))), 140 | 'key_words': tf.train.Feature( 141 | float_list=tf.train.FloatList(value=key_words.reshape(-1).astype("float"))), 142 | })) 143 | # use the proto object to serialize the example to a string 144 | serialized = example.SerializeToString() 145 | #dataLS.append(kwd_pos) 146 | # write the serialized object to disk 147 | writer.write(serialized) 148 | 149 | print 'total step: ',step -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SC-LSTM 2 | Text generation is a interesting task, and we want to generates a long text under the meaning of multiple words. In detail, given a set W = {w1, w2, ..., wk}, this generator aims at generates a text under the semantic information of those words. SC-LSTM ([Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](http://www.emnlp2015.org/proceedings/EMNLP/pdf/EMNLP199.pdf), Wen et al., 2015) is the best paper of EMNLP 2015, which is is a statistical language generator based on a semantically controlled Long Short-term Memory structure for response generation. The author incorporates a dialogue act 1-hot vector into the original LSTM model and enables the generator to output the word-related text. We directly use this model for our task. And we input a set of words represented by 1-hot vector instead of dialogue act vector in our task. 3 | 4 | The code in this repository is written in Python 2.7/TensorFlow 0.12. And if you use other versions of Python or TensorFlow, you should modify some code. Since SC-LSTM is based on original LSTM, we modify some code based on BasicLSTMCell class of TensorFlow to develop SC-LSTM model (detail in [SC_LSTM_Model.py](https://github.com/hit-computer/SC-LSTM/blob/master/SC_LSTM_Model.py)). 5 | 6 | We need text-word_set pairs to train SC-LSTM model, but to the best of our knowledge, there is no public large-scale dataset. Therefore, we can only use the public small-scale data to test this model. We have found a news article dataset annotated using AMT(More details about the corpus can be found in [the paper](http://www.cs.cmu.edu/~lmarujo/publications/lmarujo_LREC_2012.pdf)) 7 | 8 | ## Usage 9 | 10 | ### Data 11 | 12 | In `Data/` respository, there are three files `TrainingData_Keywords.txt` , `TrainingData_Text.txt` and `vec5.txt`(word embedding trained by word2vec), which is created from news article dataset mentioned above. `TrainingData_Text.txt` file contains just title, and each line is a title which is regarded as one text(data). Correspondingly, `TrainingData_keywords.txt` file contains word set, and each line is a set of word for text. Then, we use this text-words pair data to train SC-LSTM model. 13 | 14 | ### Training 15 | 16 | Before train the model, you should set some parameters of this model in `Config.py` file. Then, you need to run `Preprocess.py` file for creating `sclstm_data` file(convert trainingdata into binary formats of TensorFlow, and more detail about this can be found in [the blog](https://indico.io/blog/tensorflow-data-inputs-part1-placeholders-protobufs-queues/)), `word_vec.pkl` file(this is word embedding), `word_vec.pkl` file(vocabulary of text) and `kwd_voc.pkl` file(vocabulary of keywords). At the same time, you should set `total_step` parameter in `train.py` whose value is got from output of `Preprocess.py` 17 | 18 | Start training the model using `train.py`: 19 | 20 | ``` 21 | $ python train.py 22 | ``` 23 | 24 | ### Generation 25 | 26 | After you train the model, you can generate the text in the control of word set. You should modify `generation.py` file and set `test_word` to a set of words. Then, if you want, you can also set some parameters for generation in `Config.py` file. Generate text by run: 27 | 28 | ``` 29 | $ python generation.py 30 | ``` 31 | 32 | ## Result 33 | 34 | We randomly choose a set of words from trainingdata, `[u'FDA', u'menu']`. The training data is so small that we can't get a desired result, and some result samples show below: 35 | 36 | >Depp Calorie proposes the pregnancy END END PAD 37 | >carries FDA Have of pleading fracas END PAD 38 | >Privacy proposes FDA milk PAD END PAD PAD 39 | 40 | If you have large-scale dataset, I think you could get much better result. 41 | -------------------------------------------------------------------------------- /SC_LSTM_Model.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | import tensorflow as tf 3 | import numpy as np 4 | try: 5 | from tensorflow.python.ops.rnn_cell import BasicLSTMCell 6 | from tensorflow.python.ops.rnn_cell import DropoutWrapper 7 | from tensorflow.python.ops.rnn_cell import _linear 8 | from tensorflow.python.ops.rnn_cell import MultiRNNCell 9 | except: 10 | from tensorflow.contrib.rnn.python.ops.core_rnn_cell import BasicLSTMCell 11 | from tensorflow.contrib.rnn.python.ops.core_rnn_cell import DropoutWrapper 12 | from tensorflow.contrib.rnn.python.ops.core_rnn_cell_impl import _linear 13 | from tensorflow.contrib.rnn.python.ops.core_rnn_cell import MultiRNNCell 14 | 15 | from tensorflow.python.ops import variable_scope as vs 16 | from tensorflow.python.ops import array_ops 17 | from tensorflow.python.ops.math_ops import sigmoid 18 | from tensorflow.python.ops import nn_ops 19 | 20 | class SC_LSTM(BasicLSTMCell): 21 | def __init__(self, kwd_voc_size, *args, **kwargs): 22 | BasicLSTMCell.__init__(self, *args, **kwargs) 23 | self.key_words_voc_size = kwd_voc_size 24 | def __call__(self, inputs, state, d_act, scope=None): 25 | """Long short-term memory cell (LSTM).""" 26 | with vs.variable_scope(scope or type(self).__name__): # "BasicLSTMCell" 27 | # Parameters of gates are concatenated into one multiply for efficiency. 28 | if self._state_is_tuple: 29 | c, h = state 30 | else: 31 | try: 32 | c, h = array_ops.split(1, 2, state) 33 | except: 34 | c, h = array_ops.split(state, 2, 1) 35 | concat = _linear([inputs, h], 4 * self._num_units, True) 36 | 37 | # i = input_gate, j = new_input, f = forget_gate, o = output_gate 38 | try: 39 | i, j, f, o = array_ops.split(1, 4, concat) 40 | except: 41 | i, j, f, o = array_ops.split(concat, 4, 1) 42 | 43 | w_d = vs.get_variable('w_d', [self.key_words_voc_size, self._num_units]) 44 | 45 | new_c = (c * sigmoid(f + self._forget_bias) + sigmoid(i) * 46 | self._activation(j)) + tf.tanh(tf.matmul(d_act, w_d)) 47 | new_h = self._activation(new_c) * sigmoid(o) 48 | 49 | if self._state_is_tuple: 50 | new_state = LSTMStateTuple(new_c, new_h) 51 | else: 52 | try: 53 | new_state = array_ops.concat(1, [new_c, new_h]) 54 | except: 55 | new_state = array_ops.concat([new_c, new_h], 1) 56 | return new_h, new_state 57 | 58 | class SC_MultiRNNCell(MultiRNNCell): 59 | def __call__(self, inputs, state, d_act, scope=None): 60 | """Run this multi-layer cell on inputs, starting from state.""" 61 | with vs.variable_scope(scope or type(self).__name__): # "MultiRNNCell" 62 | cur_state_pos = 0 63 | cur_inp = inputs 64 | new_states = [] 65 | outputls = [] 66 | for i, cell in enumerate(self._cells): 67 | with vs.variable_scope("Cell%d" % i): 68 | if self._state_is_tuple: 69 | if not nest.is_sequence(state): 70 | raise ValueError( 71 | "Expected state to be a tuple of length %d, but received: %s" 72 | % (len(self.state_size), state)) 73 | cur_state = state[i] 74 | else: 75 | cur_state = array_ops.slice( 76 | state, [0, cur_state_pos], [-1, cell.state_size]) 77 | cur_state_pos += cell.state_size 78 | cur_inp, new_state = cell(cur_inp, cur_state, d_act) 79 | new_states.append(new_state) 80 | outputls.append(cur_inp) 81 | try: 82 | new_states = (tuple(new_states) if self._state_is_tuple 83 | else array_ops.concat(1, new_states)) 84 | outputs = array_ops.concat(1, outputls) 85 | except: 86 | new_states = (tuple(new_states) if self._state_is_tuple 87 | else array_ops.concat(new_states, 1)) 88 | outputs = array_ops.concat(outputls, 1) 89 | return cur_inp, new_states, outputs 90 | 91 | class SC_DropoutWrapper(DropoutWrapper): 92 | def __call__(self, inputs, state, d_act, scope=None): 93 | """Run the cell with the declared dropouts.""" 94 | if (not isinstance(self._input_keep_prob, float) or 95 | self._input_keep_prob < 1): 96 | inputs = nn_ops.dropout(inputs, self._input_keep_prob, seed=self._seed) 97 | output, new_state = self._cell(inputs, state, d_act, scope) 98 | if (not isinstance(self._output_keep_prob, float) or 99 | self._output_keep_prob < 1): 100 | output = nn_ops.dropout(output, self._output_keep_prob, seed=self._seed) 101 | return output, new_state 102 | -------------------------------------------------------------------------------- /generation.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | import tensorflow as tf 3 | import sys,time 4 | import numpy as np 5 | import cPickle, os 6 | import random 7 | import Config 8 | from SC_LSTM_Model import SC_LSTM 9 | from SC_LSTM_Model import SC_MultiRNNCell 10 | from SC_LSTM_Model import SC_DropoutWrapper 11 | 12 | test_word = [u'FDA', u'menu'] 13 | 14 | config_tf = tf.ConfigProto() 15 | config_tf.gpu_options.allow_growth = True 16 | 17 | gen_config = Config.Config() 18 | 19 | class Model(object): 20 | def __init__(self, is_training, config): 21 | self.batch_size = batch_size = config.batch_size 22 | self.num_steps = num_steps = config.num_steps 23 | self.size = size = config.hidden_size 24 | vocab_size = config.vocab_size 25 | key_words_voc_size = config.key_words_voc_size 26 | 27 | alpha = tf.constant(0.5) 28 | 29 | self._input_data = tf.placeholder(tf.int32, [batch_size, num_steps]) 30 | self._targets = tf.placeholder(tf.int32, [batch_size, num_steps]) #声明输入变量x, y 31 | self._input_word = tf.placeholder(tf.float32, [batch_size, key_words_voc_size]) 32 | self._mask = tf.placeholder(tf.float32, [batch_size, num_steps]) 33 | 34 | LSTM_cell = SC_LSTM(key_words_voc_size, size, forget_bias=0.0, state_is_tuple=False) 35 | if is_training and config.keep_prob < 1: 36 | LSTM_cell = SC_DropoutWrapper( 37 | LSTM_cell, output_keep_prob=config.keep_prob) 38 | cell = SC_MultiRNNCell([LSTM_cell] * config.num_layers, state_is_tuple=False) 39 | 40 | self._initial_state = cell.zero_state(batch_size, tf.float32) 41 | self._init_output = tf.zeros([batch_size, size*config.num_layers], tf.float32) 42 | 43 | with tf.device("/cpu:0"): 44 | embedding = tf.get_variable('word_embedding', [vocab_size, config.word_embedding_size], trainable=True) 45 | inputs = tf.nn.embedding_lookup(embedding, self._input_data) 46 | 47 | if is_training and config.keep_prob < 1: 48 | inputs = tf.nn.dropout(inputs, config.keep_prob) 49 | 50 | sc_vec = self._input_word 51 | 52 | outputs = [] 53 | output_state = self._init_output 54 | state = self._initial_state 55 | 56 | with tf.variable_scope("RNN"): 57 | for time_step in range(num_steps): 58 | with tf.variable_scope("RNN_sentence"): 59 | if time_step > 0: tf.get_variable_scope().reuse_variables() 60 | 61 | sc_wr = tf.get_variable('sc_wr',[config.word_embedding_size, key_words_voc_size]) 62 | res_wr = tf.matmul(inputs[:, time_step, :], sc_wr) 63 | 64 | res_hr = tf.zeros_like(res_wr, dtype = tf.float32) 65 | for layer_id in range(config.num_layers): 66 | sc_hr = tf.get_variable('sc_hr_%d'%layer_id,[size, key_words_voc_size]) 67 | res_hr += alpha * tf.matmul(tf.slice(output_state, [0, size*layer_id], [-1, size]), sc_hr) 68 | r_t = tf.sigmoid(res_wr + res_hr) 69 | sc_vec = r_t * sc_vec 70 | 71 | (cell_output, state, cell_outputs) = cell(inputs[:, time_step, :], state, sc_vec) 72 | outputs.append(cell_outputs) 73 | output_state = cell_outputs 74 | 75 | self._sc_vec = sc_vec 76 | self._end_output = output_state 77 | 78 | try: 79 | output = tf.reshape(tf.concat(1, outputs), [-1, size*config.num_layers]) 80 | except: 81 | output = tf.reshape(tf.concat(outputs, 1), [-1, size*config.num_layers]) 82 | softmax_w = tf.get_variable("softmax_w", [size*config.num_layers, vocab_size]) 83 | softmax_b = tf.get_variable("softmax_b", [vocab_size]) 84 | logits = tf.matmul(output, softmax_w) + softmax_b 85 | 86 | self._final_state = state 87 | self._prob = tf.nn.softmax(logits) 88 | 89 | return 90 | 91 | @property 92 | def input_data(self): 93 | return self._input_data 94 | 95 | @property 96 | def end_output(self): 97 | return self._end_output 98 | 99 | @property 100 | def targets(self): 101 | return self._targets 102 | 103 | @property 104 | def initial_state(self): 105 | return self._initial_state 106 | 107 | @property 108 | def final_state(self): 109 | return self._final_state 110 | 111 | def run_epoch(session, m, data, state=None, sc_vec=None, flag = 1, last_output=None): 112 | """Runs the model on the given data.""" 113 | x = data.reshape((1,1)) 114 | if flag == 0: 115 | prob, _state, _last_output, _sc_vec = session.run([m._prob, m.final_state, m.end_output, m._sc_vec], 116 | {m.input_data: x, 117 | m._input_word: sc_vec}) 118 | else: 119 | prob, _state, _last_output, _sc_vec = session.run([m._prob, m.final_state, m.end_output, m._sc_vec], 120 | {m.input_data: x, 121 | m._input_word: sc_vec, 122 | m.initial_state: state, 123 | m._init_output: last_output}) 124 | return prob, _state, _last_output, _sc_vec 125 | 126 | def main(_): 127 | kwd_voc = cPickle.load(open('kwd_voc.pkl','r')) 128 | gen_config.key_words_voc_size = len(kwd_voc) 129 | 130 | word_vec = cPickle.load(open('word_vec.pkl', 'r')) 131 | vocab = cPickle.load(open('word_voc.pkl','r')) 132 | 133 | word_to_idx = { ch:i for i,ch in enumerate(vocab) } 134 | idx_to_word = { i:ch for i,ch in enumerate(vocab) } 135 | keyword_to_idx = { ch:i for i,ch in enumerate(kwd_voc) } 136 | 137 | gen_config.vocab_size = len(vocab) 138 | beam_size = gen_config.BeamSize 139 | 140 | with tf.Graph().as_default(), tf.Session(config=config_tf) as session: 141 | gen_config.batch_size = 1 142 | gen_config.num_steps = 1 143 | 144 | initializer = tf.random_uniform_initializer(-gen_config.init_scale, 145 | gen_config.init_scale) 146 | with tf.variable_scope("model", reuse=None, initializer=initializer): 147 | mtest = Model(is_training=False, config=gen_config) 148 | 149 | tf.initialize_all_variables().run() 150 | 151 | model_saver = tf.train.Saver(tf.all_variables()) 152 | print 'model loading ...' 153 | model_saver.restore(session, gen_config.model_path+'--%d'%gen_config.save_time) 154 | print 'Done!' 155 | 156 | tmp = [] 157 | beams = [(0.0, [idx_to_word[1]], idx_to_word[1])] 158 | tmp = np.zeros(gen_config.key_words_voc_size) 159 | for wd in test_word: 160 | tmp[keyword_to_idx[wd]] = 1.0 161 | _input_words = np.array([tmp], dtype=np.float32) 162 | test_data = np.int32([1]) 163 | prob, _state, _last_output, _sc_vec = run_epoch(session, mtest, test_data, sc_vec=_input_words, flag=0) 164 | y1 = np.log(1e-20 + prob.reshape(-1)) 165 | if gen_config.is_sample: 166 | try: 167 | top_indices = np.random.choice(gen_config.vocab_size, beam_size, replace=False, p=prob.reshape(-1)) 168 | except: 169 | top_indices = np.random.choice(gen_config.vocab_size, beam_size, replace=True, p=prob.reshape(-1)) 170 | else: 171 | top_indices = np.argsort(-y1) 172 | b = beams[0] 173 | beam_candidates = [] 174 | for i in xrange(beam_size): 175 | wordix = top_indices[i] 176 | beam_candidates.append((b[0] + y1[wordix], b[1] + [idx_to_word[wordix]], wordix, _state, _last_output, _sc_vec)) 177 | 178 | beam_candidates.sort(key = lambda x:x[0], reverse = True) # decreasing order 179 | beams = beam_candidates[:beam_size] # truncate to get new beams 180 | 181 | for xy in range(gen_config.len_of_generation-1): 182 | beam_candidates = [] 183 | for b in beams: 184 | test_data = np.int32(b[2]) 185 | prob, _state, _last_output, _sc_vec = run_epoch(session, mtest, test_data, b[3], flag=1, last_output=b[4], sc_vec=b[5]) 186 | y1 = np.log(1e-20 + prob.reshape(-1)) 187 | if gen_config.is_sample: 188 | try: 189 | top_indices = np.random.choice(gen_config.vocab_size, beam_size, replace=False, p=prob.reshape(-1)) 190 | except: 191 | top_indices = np.random.choice(gen_config.vocab_size, beam_size, replace=True, p=prob.reshape(-1)) 192 | else: 193 | top_indices = np.argsort(-y1) 194 | #beam_candidates.append(b) 195 | for i in xrange(beam_size): 196 | wordix = top_indices[i] 197 | beam_candidates.append((b[0] + y1[wordix], b[1] + [idx_to_word[wordix]], wordix, _state, _last_output, _sc_vec)) 198 | beam_candidates.sort(key = lambda x:x[0], reverse = True) # decreasing order 199 | beams = beam_candidates[:beam_size] # truncate to get new beams 200 | 201 | print ' '.join(beams[0][1][1:]).encode('utf-8') 202 | 203 | if __name__ == "__main__": 204 | tf.app.run() 205 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | import tensorflow as tf 3 | import sys,time 4 | import numpy as np 5 | import cPickle, os 6 | import random 7 | import Config 8 | from SC_LSTM_Model import SC_LSTM 9 | from SC_LSTM_Model import SC_MultiRNNCell 10 | from SC_LSTM_Model import SC_DropoutWrapper 11 | try: 12 | from tensorflow.contrib.legacy_seq2seq.python.ops.seq2seq import sequence_loss_by_example 13 | except: 14 | pass 15 | 16 | config_tf = tf.ConfigProto() 17 | config_tf.gpu_options.allow_growth = True 18 | 19 | total_step = 29 #get value from output of Preprocess.py file 20 | 21 | class Model(object): 22 | def __init__(self, is_training, word_embedding, config, filename): 23 | self.batch_size = batch_size = config.batch_size 24 | self.num_steps = num_steps = config.num_steps 25 | self.size = size = config.hidden_size 26 | vocab_size = config.vocab_size 27 | key_words_voc_size = config.key_words_voc_size 28 | 29 | alpha = tf.constant(0.5) 30 | 31 | filename_queue = tf.train.string_input_producer([filename], 32 | num_epochs=None) 33 | # Unlike the TFRecordWriter, the TFRecordReader is symbolic 34 | reader = tf.TFRecordReader() 35 | # One can read a single serialized example from a filename 36 | # serialized_example is a Tensor of type string. 37 | _, serialized_example = reader.read(filename_queue) 38 | # The serialized example is converted back to actual values. 39 | 40 | features = tf.parse_single_example( 41 | serialized_example, 42 | features={ 43 | # We know the length of both fields. If not the 44 | # tf.VarLenFeature could be used 45 | 'input_data': tf.FixedLenFeature([batch_size*num_steps],tf.int64), 46 | 'target': tf.FixedLenFeature([batch_size*num_steps],tf.int64), 47 | 'mask': tf.FixedLenFeature([batch_size*num_steps],tf.float32), 48 | 'key_words': tf.FixedLenFeature([batch_size*key_words_voc_size],tf.float32), 49 | }) 50 | 51 | self._input_data = tf.cast(features['input_data'], tf.int32) 52 | self._targets = tf.cast(features['target'], tf.int32) #声明输入变量x, y 53 | self._mask = tf.cast(features['mask'], tf.float32) 54 | self._key_words = tf.cast(features['key_words'], tf.float32) 55 | self._input_word = tf.reshape(self._key_words, [batch_size, -1]) 56 | 57 | self._input_data = tf.reshape(self._input_data, [batch_size, -1]) 58 | self._targets = tf.reshape(self._targets, [batch_size, -1]) 59 | self._mask = tf.reshape(self._mask, [batch_size, -1]) 60 | 61 | LSTM_cell = SC_LSTM(key_words_voc_size, size, forget_bias=0.0, state_is_tuple=False) 62 | if is_training and config.keep_prob < 1: 63 | LSTM_cell = SC_DropoutWrapper( 64 | LSTM_cell, output_keep_prob=config.keep_prob) 65 | cell = SC_MultiRNNCell([LSTM_cell] * config.num_layers, state_is_tuple=False) 66 | 67 | self._initial_state = cell.zero_state(batch_size, tf.float32) 68 | self._init_output = tf.zeros([batch_size, size*config.num_layers], tf.float32) 69 | 70 | with tf.device("/cpu:0"): 71 | embedding = tf.get_variable('word_embedding', [vocab_size, config.word_embedding_size], trainable=True, initializer=tf.constant_initializer(word_embedding)) 72 | inputs = tf.nn.embedding_lookup(embedding, self._input_data) 73 | 74 | if is_training and config.keep_prob < 1: 75 | inputs = tf.nn.dropout(inputs, config.keep_prob) 76 | 77 | sc_vec = self._input_word 78 | 79 | outputs = [] 80 | output_state = self._init_output 81 | state = self._initial_state 82 | 83 | with tf.variable_scope("RNN"): 84 | for time_step in range(num_steps): 85 | with tf.variable_scope("RNN_sentence"): 86 | if time_step > 0: tf.get_variable_scope().reuse_variables() 87 | 88 | sc_wr = tf.get_variable('sc_wr',[config.word_embedding_size, key_words_voc_size]) 89 | res_wr = tf.matmul(inputs[:, time_step, :], sc_wr) 90 | 91 | res_hr = tf.zeros_like(res_wr, dtype = tf.float32) 92 | for layer_id in range(config.num_layers): 93 | sc_hr = tf.get_variable('sc_hr_%d'%layer_id,[size, key_words_voc_size]) 94 | res_hr += alpha * tf.matmul(tf.slice(output_state, [0, size*layer_id], [-1, size]), sc_hr) 95 | r_t = tf.sigmoid(res_wr + res_hr) 96 | sc_vec = r_t * sc_vec 97 | 98 | (cell_output, state, cell_outputs) = cell(inputs[:, time_step, :], state, sc_vec) 99 | outputs.append(cell_outputs) 100 | output_state = cell_outputs 101 | 102 | self._end_output = output_state 103 | 104 | try: 105 | output = tf.reshape(tf.concat(1, outputs), [-1, size*config.num_layers]) 106 | except: 107 | output = tf.reshape(tf.concat(outputs, 1), [-1, size*config.num_layers]) 108 | 109 | softmax_w = tf.get_variable("softmax_w", [size*config.num_layers, vocab_size]) 110 | softmax_b = tf.get_variable("softmax_b", [vocab_size]) 111 | logits = tf.matmul(output, softmax_w) + softmax_b 112 | try: 113 | loss = tf.nn.seq2seq.sequence_loss_by_example( 114 | [logits], 115 | [tf.reshape(self._targets, [-1])], 116 | [tf.reshape(self._mask, [-1])], average_across_timesteps=False) 117 | except: 118 | loss = sequence_loss_by_example( 119 | [logits], 120 | [tf.reshape(self._targets, [-1])], 121 | [tf.reshape(self._mask, [-1])], average_across_timesteps=False) 122 | 123 | self._cost = cost = tf.reduce_sum(loss) / batch_size 124 | self._final_state = state 125 | 126 | if not is_training: 127 | prob = tf.nn.softmax(logits) 128 | return 129 | 130 | self._lr = tf.Variable(0.0, trainable=False) 131 | tvars = tf.trainable_variables() 132 | grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars),config.max_grad_norm) 133 | optimizer = tf.train.AdamOptimizer(self.lr) 134 | self._train_op = optimizer.apply_gradients(zip(grads, tvars)) 135 | 136 | def assign_lr(self, session, lr_value): 137 | session.run(tf.assign(self.lr, lr_value)) 138 | 139 | @property 140 | def input_data(self): 141 | return self._input_data 142 | 143 | @property 144 | def end_output(self): 145 | return self._end_output 146 | 147 | @property 148 | def targets(self): 149 | return self._targets 150 | 151 | @property 152 | def initial_state(self): 153 | return self._initial_state 154 | 155 | @property 156 | def cost(self): 157 | return self._cost 158 | 159 | @property 160 | def final_state(self): 161 | return self._final_state 162 | 163 | @property 164 | def lr(self): 165 | return self._lr 166 | 167 | @property 168 | def train_op(self): 169 | return self._train_op 170 | 171 | @property 172 | def sample(self): 173 | return self._sample 174 | 175 | def run_epoch(session, m, eval_op): 176 | """Runs the model on the given data.""" 177 | start_time = time.time() 178 | costs = 0.0 179 | iters = 0 180 | #initial_output = np.zeros((m.batch_size, m.size)) 181 | for step in range(total_step+1): 182 | #state = m.initial_state.eval() 183 | cost, _ = session.run([m.cost, eval_op]) 184 | 185 | if np.isnan(cost): 186 | print 'cost is nan!!!' 187 | exit() 188 | costs += cost 189 | iters += m.num_steps 190 | 191 | if step and step % (total_step // 5) == 0: 192 | print("%d-step perplexity: %.3f cost-time: %.2f s" % 193 | (step, np.exp(costs / iters), 194 | time.time() - start_time)) 195 | start_time = time.time() 196 | 197 | return np.exp(costs / iters) 198 | 199 | def main(_): 200 | config = Config.Config() 201 | kwd_voc = cPickle.load(open('kwd_voc.pkl','r')) 202 | config.key_words_voc_size = len(kwd_voc) 203 | 204 | word_vec = cPickle.load(open('word_vec.pkl', 'r')) 205 | vocab = cPickle.load(open('word_voc.pkl','r')) 206 | 207 | config.vocab_size = len(vocab) 208 | 209 | with tf.Graph().as_default(), tf.Session(config=config_tf) as session: 210 | initializer = tf.random_uniform_initializer(-config.init_scale, 211 | config.init_scale) 212 | 213 | with tf.variable_scope("model", reuse=None, initializer=initializer): 214 | m = Model(is_training=True, word_embedding=word_vec, config=config, filename='sclstm_data') 215 | 216 | tf.global_variables_initializer().run() 217 | #tf.initialize_all_variables().run() 218 | 219 | model_saver = tf.train.Saver(tf.global_variables()) 220 | tf.train.start_queue_runners(sess=session) 221 | 222 | #model_saver = tf.train.Saver(tf.all_variables()) 223 | 224 | for i in range(config.max_max_epoch): 225 | lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0) 226 | m.assign_lr(session, config.learning_rate * lr_decay) 227 | 228 | print("Epoch: %d Learning rate: %.4f" % (i + 1, session.run(m.lr))) 229 | train_perplexity = run_epoch(session, m, m.train_op) 230 | print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity)) 231 | 232 | 233 | if (i+1) % config.save_freq == 0: 234 | print 'model saving ...' 235 | model_saver.save(session, config.model_path+'--%d'%(i+1)) 236 | print 'Done!' 237 | 238 | if __name__ == "__main__": 239 | tf.app.run() 240 | --------------------------------------------------------------------------------