├── Chapter 01
    └── NLTK Chapter 1 - Exercises.ipynb
├── Chapter 02
    ├── NLTK Chapter 2 - Exercises.ipynb
    ├── NLTK Chapter 2 - Notes.ipynb
    └── alice.txt
├── Chapter 03
    ├── NLTK Chapter 3 - Exercises.ipynb
    ├── NLTK Chapter 3 - Notes.ipynb
    ├── Nationalities.csv
    └── tranzlator.yml
├── Chapter 04
    ├── NLTK Chapter 4 - Exercises.ipynb
    └── NLTK Chapter 4 - Notes.ipynb
├── Chapter 05
    ├── NLTK Chapter 5 - Exercises.ipynb
    └── NLTK Chapter 5 - Notes.ipynb
├── Chapter 06
    └── NLTK Chapter 6 - Notes and Exercises.ipynb
├── Chapter 07
    └── NLTK Chapter 7 - Notes and Exercises.ipynb
├── Chapter 08
    ├── NLTK Chapter 8 - Exercises.ipynb
    └── NLTK Chapter 8 - Notes.ipynb
├── Chapter 09
    └── NLTK Chapter 9 - Notes and Exercises.ipynb
├── Chapter 10
    └── NLTK Chapter 10 - Notes .ipynb
├── Chapter 11
    ├── NLTK Chapter 11 - Notes & Exercises.ipynb
    └── Swadesh Lists.csv
└── README.md


/Chapter 01/NLTK Chapter 1 - Exercises.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# NLTK Chapter 1\n",
   8 |     "\n",
   9 |     "## Language Processing and Python\n",
  10 |     "\n",
  11 |     "*The html version of this chapter in the NLTK book is available [here](https://www.nltk.org/book/ch01.html#exercises \"Ch01 Exercises\").*\n",
  12 |     "\n",
  13 |     "### 8   Exercises\n",
  14 |     "\n",
  15 |     "###### 1. \n",
  16 |     "\n",
  17 |     "☼ Try using the Python interpreter as a calculator, and typing expressions like 12 / (4 + 1)."
  18 |    ]
  19 |   },
  20 |   {
  21 |    "cell_type": "code",
  22 |    "execution_count": 2,
  23 |    "metadata": {},
  24 |    "outputs": [
  25 |     {
  26 |      "data": {
  27 |       "text/plain": [
  28 |        "8"
  29 |       ]
  30 |      },
  31 |      "execution_count": 2,
  32 |      "metadata": {},
  33 |      "output_type": "execute_result"
  34 |     }
  35 |    ],
  36 |    "source": [
  37 |     "8008 // ((10 * 10 * 10) + 1)"
  38 |    ]
  39 |   },
  40 |   {
  41 |    "cell_type": "markdown",
  42 |    "metadata": {},
  43 |    "source": [
  44 |     "###### 2. \n",
  45 |     "\n",
  46 |     "☼ Given an alphabet of 26 letters, there are 26 to the power 10, or `26 ** 10`, ten-letter strings we can form. That works out to `141167095653376`. How many hundred-letter strings are possible?\n",
  47 |     "\n",
  48 |     "*`print(\"{}\".format)` has not yet been introduced in the book, but it can be used to make strings - especially numerical ones - much more legible.*"
  49 |    ]
  50 |   },
  51 |   {
  52 |    "cell_type": "code",
  53 |    "execution_count": 4,
  54 |    "metadata": {},
  55 |    "outputs": [
  56 |     {
  57 |      "name": "stdout",
  58 |      "output_type": "stream",
  59 |      "text": [
  60 |       "3,142,930,641,582,938,830,174,357,788,501,626,427,282,669,988,762,475,256,374,173,175,398,995,908,420,104,023,465,432,599,069,702,289,330,964,075,081,611,719,197,835,869,803,511,992,549,376\n"
  61 |      ]
  62 |     }
  63 |    ],
  64 |    "source": [
  65 |     "print(\"{:,}\".format(26 ** 100))"
  66 |    ]
  67 |   },
  68 |   {
  69 |    "cell_type": "markdown",
  70 |    "metadata": {},
  71 |    "source": [
  72 |     "###### 3. \n",
  73 |     "☼ The Python multiplication operation can be applied to lists. What happens when you type `['Monty', 'Python'] * 20`, or `3 * sent1`?"
  74 |    ]
  75 |   },
  76 |   {
  77 |    "cell_type": "code",
  78 |    "execution_count": 13,
  79 |    "metadata": {},
  80 |    "outputs": [
  81 |     {
  82 |      "name": "stdout",
  83 |      "output_type": "stream",
  84 |      "text": [
  85 |       "['Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python', 'Monty', 'Python']"
  86 |      ]
  87 |     }
  88 |    ],
  89 |    "source": [
  90 |     "print(['Monty', 'Python'] * 20, end = '')"
  91 |    ]
  92 |   },
  93 |   {
  94 |    "cell_type": "code",
  95 |    "execution_count": 10,
  96 |    "metadata": {},
  97 |    "outputs": [
  98 |     {
  99 |      "name": "stdout",
 100 |      "output_type": "stream",
 101 |      "text": [
 102 |       "*** Introductory Examples for the NLTK Book ***\n",
 103 |       "Loading text1, ..., text9 and sent1, ..., sent9\n",
 104 |       "Type the name of the text or sentence to view it.\n",
 105 |       "Type: 'texts()' or 'sents()' to list the materials.\n",
 106 |       "text1: Moby Dick by Herman Melville 1851\n",
 107 |       "text2: Sense and Sensibility by Jane Austen 1811\n",
 108 |       "text3: The Book of Genesis\n",
 109 |       "text4: Inaugural Address Corpus\n",
 110 |       "text5: Chat Corpus\n",
 111 |       "text6: Monty Python and the Holy Grail\n",
 112 |       "text7: Wall Street Journal\n",
 113 |       "text8: Personals Corpus\n",
 114 |       "text9: The Man Who Was Thursday by G . K . Chesterton 1908\n"
 115 |      ]
 116 |     }
 117 |    ],
 118 |    "source": [
 119 |     "import nltk\n",
 120 |     "\n",
 121 |     "from nltk.book import *\n"
 122 |    ]
 123 |   },
 124 |   {
 125 |    "cell_type": "code",
 126 |    "execution_count": 12,
 127 |    "metadata": {},
 128 |    "outputs": [
 129 |     {
 130 |      "name": "stdout",
 131 |      "output_type": "stream",
 132 |      "text": [
 133 |       "['Call', 'me', 'Ishmael', '.', 'Call', 'me', 'Ishmael', '.', 'Call', 'me', 'Ishmael', '.']"
 134 |      ]
 135 |     }
 136 |    ],
 137 |    "source": [
 138 |     "print(3 * sent1, end = '')"
 139 |    ]
 140 |   },
 141 |   {
 142 |    "cell_type": "markdown",
 143 |    "metadata": {},
 144 |    "source": [
 145 |     "###### 4.\n",
 146 |     "\n",
 147 |     "☼ Review [1](https://www.nltk.org/book/ch01.html#sec-computing-with-language-texts-and-words \"computing with language\") on computing with language. How many words are there in `text2`? How many distinct words are there?"
 148 |    ]
 149 |   },
 150 |   {
 151 |    "cell_type": "code",
 152 |    "execution_count": 30,
 153 |    "metadata": {},
 154 |    "outputs": [
 155 |     {
 156 |      "data": {
 157 |       "text/plain": [
 158 |        "6833"
 159 |       ]
 160 |      },
 161 |      "execution_count": 30,
 162 |      "metadata": {},
 163 |      "output_type": "execute_result"
 164 |     }
 165 |    ],
 166 |    "source": [
 167 |     "# number of words\n",
 168 |     "\n",
 169 |     "len(set(text2))"
 170 |    ]
 171 |   },
 172 |   {
 173 |    "cell_type": "markdown",
 174 |    "metadata": {},
 175 |    "source": [
 176 |     "*This isn't entirely accurate, as punctuation marks(e.g., periods, commas, etc...) are included as tokens.  To eliminate non-alphabetic strings, we can use this list comprehension with the method `.isalpha()`.* \n",
 177 |     "\n",
 178 |     "*However, another problem is that the tokenizer splits words with apostrophes (i.e., contractions and possessives) into their constituent parts, so that a word such as `didn't` would become `didn`, `'`, and `t`.  `.isalpha()` will remove apostrophes from the count, but it would be inaccurate to separately count all the enclitics `d`, `ll`, `m`, `re`, `s`, `t`, `ve`s that result from tokenization.  Therefore, we'll add the boolean `and not in (\"d\", \"ll\", \"m\", \"re\", \"s\", \"t\", \"ve\")` to the list comprehension to account for these:*"
 179 |    ]
 180 |   },
 181 |   {
 182 |    "cell_type": "code",
 183 |    "execution_count": 31,
 184 |    "metadata": {},
 185 |    "outputs": [
 186 |     {
 187 |      "data": {
 188 |       "text/plain": [
 189 |        "6713"
 190 |       ]
 191 |      },
 192 |      "execution_count": 31,
 193 |      "metadata": {},
 194 |      "output_type": "execute_result"
 195 |     }
 196 |    ],
 197 |    "source": [
 198 |     "len(set([w for w in text2 if w.isalpha()]))"
 199 |    ]
 200 |   },
 201 |   {
 202 |    "cell_type": "code",
 203 |    "execution_count": 307,
 204 |    "metadata": {},
 205 |    "outputs": [
 206 |     {
 207 |      "data": {
 208 |       "text/plain": [
 209 |        "6706"
 210 |       ]
 211 |      },
 212 |      "execution_count": 307,
 213 |      "metadata": {},
 214 |      "output_type": "execute_result"
 215 |     }
 216 |    ],
 217 |    "source": [
 218 |     "len(set([w for w in text2 if w.isalpha() and w not in (\"d\", \"ll\", \"m\", \"re\", \"s\", \"t\", \"ve\")]))"
 219 |    ]
 220 |   },
 221 |   {
 222 |    "cell_type": "markdown",
 223 |    "metadata": {},
 224 |    "source": [
 225 |     "*If by chance we want to include numbers:*"
 226 |    ]
 227 |   },
 228 |   {
 229 |    "cell_type": "code",
 230 |    "execution_count": 33,
 231 |    "metadata": {},
 232 |    "outputs": [
 233 |     {
 234 |      "data": {
 235 |       "text/plain": [
 236 |        "6762"
 237 |       ]
 238 |      },
 239 |      "execution_count": 33,
 240 |      "metadata": {},
 241 |      "output_type": "execute_result"
 242 |     }
 243 |    ],
 244 |    "source": [
 245 |     "len(set([w for w in text2 if w.isalnum() and w not in (\"d\", \"ll\", \"s\", \"t\")]))"
 246 |    ]
 247 |   },
 248 |   {
 249 |    "cell_type": "markdown",
 250 |    "metadata": {},
 251 |    "source": [
 252 |     "###### 5.\n",
 253 |     "\n",
 254 |     "☼ Compare the lexical diversity scores for humor and romance fiction in [1.1](https://www.nltk.org/book/ch01.html#tab-brown-types \"Brown types\"). Which genre is more lexically diverse?\n",
 255 |     "\n",
 256 |     "*Humor (0.231) is about twice as lexically diverse as romance fiction (0.121).*"
 257 |    ]
 258 |   },
 259 |   {
 260 |    "cell_type": "markdown",
 261 |    "metadata": {},
 262 |    "source": [
 263 |     "###### 6. \n",
 264 |     "\n",
 265 |     "☼ Produce a dispersion plot of the four main protagonists in *Sense and Sensibility*: Elinor, Marianne, Edward, and Willoughby. What can you observe about the different roles played by the males and females in this novel? Can you identify the couples?"
 266 |    ]
 267 |   },
 268 |   {
 269 |    "cell_type": "code",
 270 |    "execution_count": 35,
 271 |    "metadata": {},
 272 |    "outputs": [
 273 |     {
 274 |      "data": {
 275 |       "image/png": 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\n",
 276 |       "text/plain": [
 277 |        "<Figure size 432x288 with 1 Axes>"
 278 |       ]
 279 |      },
 280 |      "metadata": {
 281 |       "needs_background": "light"
 282 |      },
 283 |      "output_type": "display_data"
 284 |     }
 285 |    ],
 286 |    "source": [
 287 |     "%matplotlib inline \n",
 288 |     "\n",
 289 |     "text2.dispersion_plot([\"Elinor\", \"Marianne\", \"Edward\", \"Willoughby\"])"
 290 |    ]
 291 |   },
 292 |   {
 293 |    "cell_type": "markdown",
 294 |    "metadata": {},
 295 |    "source": [
 296 |     "*I haven't read anything by Jane Austen, but it appears that the female characters dominate the novel.  It also appears that Elinor and Edward are a couple, as it seems Marianne and Willoughby are.*"
 297 |    ]
 298 |   },
 299 |   {
 300 |    "cell_type": "markdown",
 301 |    "metadata": {},
 302 |    "source": [
 303 |     "###### 7.\n",
 304 |     "\n",
 305 |     "☼ Find the collocations in text5."
 306 |    ]
 307 |   },
 308 |   {
 309 |    "cell_type": "code",
 310 |    "execution_count": 36,
 311 |    "metadata": {},
 312 |    "outputs": [
 313 |     {
 314 |      "name": "stdout",
 315 |      "output_type": "stream",
 316 |      "text": [
 317 |       "wanna chat; PART JOIN; MODE #14-19teens; JOIN PART; PART PART;\n",
 318 |       "cute.-ass MP3; MP3 player; JOIN JOIN; times .. .; ACTION watches; guys\n",
 319 |       "wanna; song lasts; last night; ACTION sits; -...)...- S.M.R.; Lime\n",
 320 |       "Player; Player 12%; dont know; lez gurls; long time\n"
 321 |      ]
 322 |     }
 323 |    ],
 324 |    "source": [
 325 |     "text5.collocations()"
 326 |    ]
 327 |   },
 328 |   {
 329 |    "cell_type": "markdown",
 330 |    "metadata": {},
 331 |    "source": [
 332 |     "###### 8. \n",
 333 |     "\n",
 334 |     "☼ Consider the following Python expression: `len(set(text4))`. State the purpose of this expression. Describe the two steps involved in performing this computation."
 335 |    ]
 336 |   },
 337 |   {
 338 |    "cell_type": "code",
 339 |    "execution_count": 38,
 340 |    "metadata": {},
 341 |    "outputs": [
 342 |     {
 343 |      "data": {
 344 |       "text/plain": [
 345 |        "9913"
 346 |       ]
 347 |      },
 348 |      "execution_count": 38,
 349 |      "metadata": {},
 350 |      "output_type": "execute_result"
 351 |     }
 352 |    ],
 353 |    "source": [
 354 |     "len(set(text4))"
 355 |    ]
 356 |   },
 357 |   {
 358 |    "cell_type": "markdown",
 359 |    "metadata": {},
 360 |    "source": [
 361 |     "*This expression returns the number of types (unique strings) in `text4`.  `set` compiles all the unique strings in `text4`, and `len` returns the number of items in that set.*"
 362 |    ]
 363 |   },
 364 |   {
 365 |    "cell_type": "markdown",
 366 |    "metadata": {},
 367 |    "source": [
 368 |     "###### 9.\n",
 369 |     "\n",
 370 |     "☼ Review [2](https://www.nltk.org/book/ch01.html#sec-a-closer-look-at-python-texts-as-lists-of-words \"Lists and strings\") on lists and strings.\n",
 371 |     "\n",
 372 |     "+ a. Define a string and assign it to a variable, e.g.,` my_string = 'My String'` (but put something more interesting in the string). Print the contents of this variable in two ways, first by simply typing the variable name and pressing enter, then by using the `print` statement.\n",
 373 |     "\n",
 374 |     "+ b. Try adding the string to itself using `my_string + my_string`, or multiplying it by a number, e.g., `my_string * 3`. Notice that the strings are joined together without any spaces. How could you fix this?\n"
 375 |    ]
 376 |   },
 377 |   {
 378 |    "cell_type": "code",
 379 |    "execution_count": 39,
 380 |    "metadata": {},
 381 |    "outputs": [
 382 |     {
 383 |      "data": {
 384 |       "text/plain": [
 385 |        "\"Something more interesting than 'My String'\""
 386 |       ]
 387 |      },
 388 |      "execution_count": 39,
 389 |      "metadata": {},
 390 |      "output_type": "execute_result"
 391 |     }
 392 |    ],
 393 |    "source": [
 394 |     "# a\n",
 395 |     "\n",
 396 |     "my_string = \"Something more interesting than 'My String'\"\n",
 397 |     "\n",
 398 |     "my_string"
 399 |    ]
 400 |   },
 401 |   {
 402 |    "cell_type": "code",
 403 |    "execution_count": 40,
 404 |    "metadata": {},
 405 |    "outputs": [
 406 |     {
 407 |      "name": "stdout",
 408 |      "output_type": "stream",
 409 |      "text": [
 410 |       "Something more interesting than 'My String'\n"
 411 |      ]
 412 |     }
 413 |    ],
 414 |    "source": [
 415 |     "print(my_string)"
 416 |    ]
 417 |   },
 418 |   {
 419 |    "cell_type": "code",
 420 |    "execution_count": 41,
 421 |    "metadata": {},
 422 |    "outputs": [
 423 |     {
 424 |      "data": {
 425 |       "text/plain": [
 426 |        "\"Something more interesting than 'My String'Something more interesting than 'My String'Something more interesting than 'My String'\""
 427 |       ]
 428 |      },
 429 |      "execution_count": 41,
 430 |      "metadata": {},
 431 |      "output_type": "execute_result"
 432 |     }
 433 |    ],
 434 |    "source": [
 435 |     "# b\n",
 436 |     "\n",
 437 |     "my_string * 3"
 438 |    ]
 439 |   },
 440 |   {
 441 |    "cell_type": "code",
 442 |    "execution_count": 44,
 443 |    "metadata": {},
 444 |    "outputs": [
 445 |     {
 446 |      "name": "stdout",
 447 |      "output_type": "stream",
 448 |      "text": [
 449 |       "Something more interesting than 'My String' Something more interesting than 'My String' Something more interesting than 'My String' \n"
 450 |      ]
 451 |     }
 452 |    ],
 453 |    "source": [
 454 |     "print((my_string + \" \") * 3)"
 455 |    ]
 456 |   },
 457 |   {
 458 |    "cell_type": "code",
 459 |    "execution_count": 49,
 460 |    "metadata": {},
 461 |    "outputs": [
 462 |     {
 463 |      "name": "stdout",
 464 |      "output_type": "stream",
 465 |      "text": [
 466 |       "Something more interesting than 'My String' Something more interesting than 'My String' Something more interesting than 'My String'\n"
 467 |      ]
 468 |     }
 469 |    ],
 470 |    "source": [
 471 |     "print(' '.join([my_string] * 3))"
 472 |    ]
 473 |   },
 474 |   {
 475 |    "cell_type": "markdown",
 476 |    "metadata": {},
 477 |    "source": [
 478 |     "###### 10.\n",
 479 |     "\n",
 480 |     "☼ Define a variable my_sent to be a list of words, using the syntax `my_sent = [\"My\", \"sent\"]` (but with your own words, or a favorite saying).\n",
 481 |     "\n",
 482 |     "+ a. Use `' '.join(my_sent)` to convert this into a string.\n",
 483 |     "    \n",
 484 |     "+ b. Use `split()` to split the string back into the list form you had to start with."
 485 |    ]
 486 |   },
 487 |   {
 488 |    "cell_type": "code",
 489 |    "execution_count": 52,
 490 |    "metadata": {},
 491 |    "outputs": [
 492 |     {
 493 |      "data": {
 494 |       "text/plain": [
 495 |        "\"You can lead a horse to water but you can't saddle a duck.\""
 496 |       ]
 497 |      },
 498 |      "execution_count": 52,
 499 |      "metadata": {},
 500 |      "output_type": "execute_result"
 501 |     }
 502 |    ],
 503 |    "source": [
 504 |     "my_sent = [\"You\", \"can\", \"lead\", \"a\", \"horse\", \"to\", \"water\", \"but\", \n",
 505 |     "           \"you\", \"can't\", \"saddle\", \"a\", \"duck.\"]\n",
 506 |     "\n",
 507 |     "my_string = ' '.join(my_sent)\n",
 508 |     "my_string"
 509 |    ]
 510 |   },
 511 |   {
 512 |    "cell_type": "code",
 513 |    "execution_count": 58,
 514 |    "metadata": {},
 515 |    "outputs": [
 516 |     {
 517 |      "name": "stdout",
 518 |      "output_type": "stream",
 519 |      "text": [
 520 |       "['You', 'can', 'lead', 'a', 'horse', 'to', 'water', 'but', 'you', \"can't\", 'saddle', 'a', 'duck.'] "
 521 |      ]
 522 |     }
 523 |    ],
 524 |    "source": [
 525 |     "print(my_string.split(), end = ' ')"
 526 |    ]
 527 |   },
 528 |   {
 529 |    "cell_type": "markdown",
 530 |    "metadata": {},
 531 |    "source": [
 532 |     "###### 11.\n",
 533 |     "\n",
 534 |     "☼ Define several variables containing lists of words, e.g., `phrase1`, `phrase2`, and so on. Join them together in various combinations (using the plus operator) to form whole sentences. What is the relationship between `len(phrase1 + phrase2)` and `len(phrase1) + len(phrase2)`?"
 535 |    ]
 536 |   },
 537 |   {
 538 |    "cell_type": "code",
 539 |    "execution_count": 60,
 540 |    "metadata": {},
 541 |    "outputs": [
 542 |     {
 543 |      "name": "stdout",
 544 |      "output_type": "stream",
 545 |      "text": [
 546 |       "I once shot an elephant in my pajamas. What he was doing in my pajamas I don't know.\n"
 547 |      ]
 548 |     }
 549 |    ],
 550 |    "source": [
 551 |     "phrase1 = [\"I\", \"once\", \"shot\", \"an\", \"elephant\", \"in\", \"my\", \"pajamas.\"]\n",
 552 |     "phrase2 = [\"What\", \"he\", \"was\", \"doing\", \"in\", \"my\", \"pajamas\", \n",
 553 |     "           \"I\", \"don't\", \"know.\"]\n",
 554 |     "\n",
 555 |     "print(' '.join(phrase1 + phrase2))"
 556 |    ]
 557 |   },
 558 |   {
 559 |    "cell_type": "code",
 560 |    "execution_count": 61,
 561 |    "metadata": {},
 562 |    "outputs": [
 563 |     {
 564 |      "data": {
 565 |       "text/plain": [
 566 |        "18"
 567 |       ]
 568 |      },
 569 |      "execution_count": 61,
 570 |      "metadata": {},
 571 |      "output_type": "execute_result"
 572 |     }
 573 |    ],
 574 |    "source": [
 575 |     "len(phrase1 + phrase2)"
 576 |    ]
 577 |   },
 578 |   {
 579 |    "cell_type": "code",
 580 |    "execution_count": 62,
 581 |    "metadata": {},
 582 |    "outputs": [
 583 |     {
 584 |      "data": {
 585 |       "text/plain": [
 586 |        "18"
 587 |       ]
 588 |      },
 589 |      "execution_count": 62,
 590 |      "metadata": {},
 591 |      "output_type": "execute_result"
 592 |     }
 593 |    ],
 594 |    "source": [
 595 |     "len(phrase1) + len(phrase2)"
 596 |    ]
 597 |   },
 598 |   {
 599 |    "cell_type": "markdown",
 600 |    "metadata": {},
 601 |    "source": [
 602 |     "*The lengths are obviously the same.*"
 603 |    ]
 604 |   },
 605 |   {
 606 |    "cell_type": "markdown",
 607 |    "metadata": {},
 608 |    "source": [
 609 |     "###### 12. \n",
 610 |     "\n",
 611 |     "☼ Consider the following two expressions, which have the same value. Which one will typically be more relevant in NLP? Why?\n",
 612 |     "\n",
 613 |     "+ a. `\"Monty Python\"[6:12]`\n",
 614 |     "    \n",
 615 |     "+ b. `[\"Monty\", \"Python\"][1]`\n",
 616 |     "    \n",
 617 |     "*List indexing will surely be more relevant than string indexing in NLP.  We're much more likely to be dealing with long lists of individual words than long strings of individual characters.*\n",
 618 |     "\n",
 619 |     "###### 13. \n",
 620 |     "\n",
 621 |     "☼ We have seen how to represent a sentence as a list of words, where each word is a sequence of characters. What does `sent1[2][2]` do? Why? Experiment with other index values."
 622 |    ]
 623 |   },
 624 |   {
 625 |    "cell_type": "code",
 626 |    "execution_count": 63,
 627 |    "metadata": {},
 628 |    "outputs": [
 629 |     {
 630 |      "data": {
 631 |       "text/plain": [
 632 |        "'h'"
 633 |       ]
 634 |      },
 635 |      "execution_count": 63,
 636 |      "metadata": {},
 637 |      "output_type": "execute_result"
 638 |     }
 639 |    ],
 640 |    "source": [
 641 |     "sent1[2][2]"
 642 |    ]
 643 |   },
 644 |   {
 645 |    "cell_type": "markdown",
 646 |    "metadata": {},
 647 |    "source": [
 648 |     "`sent1[2][2]` returns the third letter of the third word in `sent1`."
 649 |    ]
 650 |   },
 651 |   {
 652 |    "cell_type": "code",
 653 |    "execution_count": 65,
 654 |    "metadata": {},
 655 |    "outputs": [
 656 |     {
 657 |      "data": {
 658 |       "text/plain": [
 659 |        "'e'"
 660 |       ]
 661 |      },
 662 |      "execution_count": 65,
 663 |      "metadata": {},
 664 |      "output_type": "execute_result"
 665 |     }
 666 |    ],
 667 |    "source": [
 668 |     "sent9[-2][-2]"
 669 |    ]
 670 |   },
 671 |   {
 672 |    "cell_type": "markdown",
 673 |    "metadata": {},
 674 |    "source": [
 675 |     "###### 14. \n",
 676 |     "\n",
 677 |     "☼ The first sentence of `text3` is provided to you in the variable `sent3`. The index of *the* in `sent3` is 1, because `sent3[1]` gives us `'the'`. What are the indexes of the two other occurrences of this word in `sent3`?\n",
 678 |     "\n",
 679 |     "*There's no easy way to do this using only those methods introduced so far in the book, since `index` will only return the first instance of an item.  We could cheat and just print the sentence, manually counting the indices of the instances:*"
 680 |    ]
 681 |   },
 682 |   {
 683 |    "cell_type": "code",
 684 |    "execution_count": 82,
 685 |    "metadata": {},
 686 |    "outputs": [
 687 |     {
 688 |      "name": "stdout",
 689 |      "output_type": "stream",
 690 |      "text": [
 691 |       "['In', 'the', 'beginning', 'God', 'created', 'the', 'heaven', 'and', 'the', 'earth', '.'] "
 692 |      ]
 693 |     }
 694 |    ],
 695 |    "source": [
 696 |     "print(sent3, end = ' ')"
 697 |    ]
 698 |   },
 699 |   {
 700 |    "cell_type": "markdown",
 701 |    "metadata": {},
 702 |    "source": [
 703 |     "*But I highly doubt that's what the authors had in mind.  Instead, we can take advantage of the fact that `index` allows us to specify as arguments where the search should begin and end.  Then we can use a `for`-loop and a conditional to iteratively begin the search just after the point where the previous instance of `the` was found:*"
 704 |    ]
 705 |   },
 706 |   {
 707 |    "cell_type": "code",
 708 |    "execution_count": 84,
 709 |    "metadata": {},
 710 |    "outputs": [
 711 |     {
 712 |      "name": "stdout",
 713 |      "output_type": "stream",
 714 |      "text": [
 715 |       "1\n",
 716 |       "5\n",
 717 |       "8\n"
 718 |      ]
 719 |     }
 720 |    ],
 721 |    "source": [
 722 |     "i = 0\n",
 723 |     "for w in sent3:\n",
 724 |     "    if w == 'the':\n",
 725 |     "        i = sent3.index(w, i, -1)\n",
 726 |     "        print(i)\n",
 727 |     "        i += 1"
 728 |    ]
 729 |   },
 730 |   {
 731 |    "cell_type": "markdown",
 732 |    "metadata": {},
 733 |    "source": [
 734 |     "*I don't believe that iterators have been covered in the book yet.  If you haven't seen them before, `i += 1` just adds one to the current value of `i`.  It's basically a shortcut for `i = i + 1`.*\n",
 735 |     "\n",
 736 |     "*For what it's worth, we don't need to specify the ending point of the search:*"
 737 |    ]
 738 |   },
 739 |   {
 740 |    "cell_type": "code",
 741 |    "execution_count": 85,
 742 |    "metadata": {},
 743 |    "outputs": [
 744 |     {
 745 |      "name": "stdout",
 746 |      "output_type": "stream",
 747 |      "text": [
 748 |       "1\n",
 749 |       "5\n",
 750 |       "8\n"
 751 |      ]
 752 |     }
 753 |    ],
 754 |    "source": [
 755 |     "i = 0\n",
 756 |     "for w in sent3:\n",
 757 |     "    if w == 'the':\n",
 758 |     "        i = sent3.index(w, i)\n",
 759 |     "        print(i)\n",
 760 |     "        i += 1"
 761 |    ]
 762 |   },
 763 |   {
 764 |    "cell_type": "markdown",
 765 |    "metadata": {},
 766 |    "source": [
 767 |     "###### 15. \n",
 768 |     "\n",
 769 |     "☼ Review the discussion of conditionals in 4. Find all words in the Chat Corpus (`text5`) starting with the letter *b*. Show them in alphabetical order.\n",
 770 |     "\n",
 771 |     "*We need to find words that begin with either `b` or `B`, so we'll use the tuple `('b', 'B')` with `.startswith()` to make sure matches for both characters are returned.  Also, `sorted` will always place words beginning with uppercase letters before those starting with lowercase ones, so to deactivate this behavior we have to add the argument `key = str.casefold`.*"
 772 |    ]
 773 |   },
 774 |   {
 775 |    "cell_type": "code",
 776 |    "execution_count": 95,
 777 |    "metadata": {},
 778 |    "outputs": [
 779 |     {
 780 |      "name": "stdout",
 781 |      "output_type": "stream",
 782 |      "text": [
 783 |       "['B', 'b', 'b-day', 'b/c', 'b4', 'BAAAAALLLLLLLLIIIIIIINNNNNNNNNNN', 'babay', 'babble', 'babblein', 'babe', 'babes', 'babi', 'babies', 'babiess', 'baby', 'babycakeses', 'bachelorette', 'back', 'Back', 'backatchya', 'backfrontsidewaysandallaroundtheworld', 'backroom', 'backup', 'bacl', 'bad', 'bag', 'bagel', 'bagels', 'bahahahaa', 'bak', 'baked', 'balad', 'balance', 'balck', 'ball', 'ballin', 'balls', 'ban', 'band', 'bandito', 'bandsaw', 'banjoes', 'banned', 'baord', 'bar', 'barbie', 'Barbieee', 'bare', 'barely', 'bares', 'barfights', 'barks', 'barn', 'Barometer', 'barrel', 'base', 'bases', 'basically', 'basket', 'battery', 'bay', 'bbbbbyyyyyyyeeeeeeeee', 'bbiam', 'bbl', 'bbs', 'bc', 'BE', 'be', 'Beach', 'beach', 'beachhhh', 'beam', 'beams', 'beanbag', 'beans', 'bear', 'bears', 'beat', 'beaten', 'beatles', 'beats', 'beattles', 'beautiful', 'because', 'Because', 'beckley', 'become', 'bed', 'bedford', 'bedroom', 'beeeeehave', 'beeehave', 'Been', 'been', 'beer', 'before', 'beg', 'begin', 'behave', 'behind', 'bein', 'being', 'beleive', 'believe', 'belive', 'bell', 'belly', 'belong', 'belongings', 'ben', 'Ben', 'bend', 'Benjamin', 'benz', 'bes', 'beside', 'besides', 'best', 'bet', 'betrayal', 'betta', 'better', 'Better', 'between', 'beuty', 'bf', 'bi', 'biatch', 'Bible', 'bible', 'biebsa', 'bied', 'big', 'BIG', 'bigest', 'biggest', 'biiiatch', 'Biiiiiitch', 'bike', 'bikes', 'bikini', 'bio', 'Biographys', 'bird', 'Birdgang', 'birfday', 'birthday', 'bisexual', 'bishes', 'bit', 'bitch', 'bitches', 'bitdh', 'bite', 'bites', 'biyatch', 'biz', 'bj', 'black', 'blade', 'blah', 'blank', 'blankie', 'blazed', 'bleach', 'blech', 'bless', 'blessings', 'blew', 'blind', 'blinks', 'bliss', 'blocking', 'bloe', 'BLONDES', 'blood', 'blooded', 'bloody', 'Bloooooooood', 'Bloooooooooood', 'Bloooooooooooood', 'blow', 'blowing', 'blowjob', 'blowup', 'blue', 'blueberry', 'bluer', 'blues', 'blunt', 'board', 'bob', 'bodies', 'body', 'boed', 'boght', 'boi', 'boing', 'boinked', 'bois', 'bomb', 'bone', 'Bone', 'boned', 'bones', 'bong', 'boning', 'Bonus', 'bonus', 'boo', 'booboo', 'boobs', 'book', 'Books', 'boom', 'Boone', 'boooooooooooglyyyyyy', 'boost', 'boot', 'bootay', 'booted', 'boots', 'BOOTS', 'booty', 'BOOTY', 'Booyah', 'Borat', 'border', 'borderline', 'bored', 'boredom', 'boring', 'Born', 'born', 'born-again', 'bosom', 'boss', 'bossy', 'bot', 'both', 'bother', 'bothering', 'bottle', 'bought', 'bounced', 'bouncer', 'bouncers', 'bound', 'bout', 'bouts', 'bow', 'bowl', 'box', 'Box', 'boy', 'BOY', 'boyfriend', 'boys', 'Boyz', 'bra', 'brad', 'brady', 'brain', 'brakes', 'brass', 'brat', 'brb', 'brbbb', 'bread', 'Break', 'break', 'Breaking', 'breaks', 'breath', 'breathe', 'bred', 'breeding', 'bright', 'brightened', 'bring', 'brings', 'bro', 'broke', 'Broken', 'brooklyn', 'brother', 'brothers', 'brought', 'brown', 'brrrrrrr', 'bruises', 'brunswick', 'brwn', 'btw', 'bucks', 'Bud', 'buddyyyyyy', 'buff', 'buffalo', 'bug', 'bugs', 'buh', 'build', 'builds', 'built', 'bull', 'bulls', 'bum', 'bumber', 'bummer', 'bumped', 'bumper', 'bunch', 'bunny', 'Burger', 'burger', 'burito', 'burned', 'burns', 'burp', 'burpin', 'burps', 'burried', 'burryed', 'bus', 'buses', 'bust', 'busted', 'busy', 'BUt', 'But', 'but', 'BUT', 'butt', 'butter', 'butterscotch', 'button', 'buttons', 'buy', 'buying', 'bwahahahahahahahahahaha', 'Bwhaha', 'by', 'byb', 'bye', 'BYE', 'Bye', 'byeee', 'byeeee', 'byeeeeeeee', 'byeeeeeeeeeeeee', 'byes']\n"
 784 |      ]
 785 |     }
 786 |    ],
 787 |    "source": [
 788 |     "\n",
 789 |     "print(sorted(set([w for w in text5 if w.startswith(('b', 'B'))]), key = str.casefold))"
 790 |    ]
 791 |   },
 792 |   {
 793 |    "cell_type": "markdown",
 794 |    "metadata": {},
 795 |    "source": [
 796 |     "16. ☼ Type the expression `list(range(10))` at the interpreter prompt. Now try `list(range(10, 20))`, `list(range(10, 20, 2))`, and `list(range(20, 10, -2))`. We will see a variety of uses for this built-in function in later chapters."
 797 |    ]
 798 |   },
 799 |   {
 800 |    "cell_type": "code",
 801 |    "execution_count": 96,
 802 |    "metadata": {},
 803 |    "outputs": [
 804 |     {
 805 |      "data": {
 806 |       "text/plain": [
 807 |        "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
 808 |       ]
 809 |      },
 810 |      "execution_count": 96,
 811 |      "metadata": {},
 812 |      "output_type": "execute_result"
 813 |     }
 814 |    ],
 815 |    "source": [
 816 |     "list(range(10))"
 817 |    ]
 818 |   },
 819 |   {
 820 |    "cell_type": "code",
 821 |    "execution_count": 97,
 822 |    "metadata": {},
 823 |    "outputs": [
 824 |     {
 825 |      "data": {
 826 |       "text/plain": [
 827 |        "[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
 828 |       ]
 829 |      },
 830 |      "execution_count": 97,
 831 |      "metadata": {},
 832 |      "output_type": "execute_result"
 833 |     }
 834 |    ],
 835 |    "source": [
 836 |     "list(range(10, 20))"
 837 |    ]
 838 |   },
 839 |   {
 840 |    "cell_type": "code",
 841 |    "execution_count": 98,
 842 |    "metadata": {},
 843 |    "outputs": [
 844 |     {
 845 |      "data": {
 846 |       "text/plain": [
 847 |        "[10, 12, 14, 16, 18]"
 848 |       ]
 849 |      },
 850 |      "execution_count": 98,
 851 |      "metadata": {},
 852 |      "output_type": "execute_result"
 853 |     }
 854 |    ],
 855 |    "source": [
 856 |     "list(range(10, 20, 2))"
 857 |    ]
 858 |   },
 859 |   {
 860 |    "cell_type": "code",
 861 |    "execution_count": 99,
 862 |    "metadata": {},
 863 |    "outputs": [
 864 |     {
 865 |      "data": {
 866 |       "text/plain": [
 867 |        "[20, 18, 16, 14, 12]"
 868 |       ]
 869 |      },
 870 |      "execution_count": 99,
 871 |      "metadata": {},
 872 |      "output_type": "execute_result"
 873 |     }
 874 |    ],
 875 |    "source": [
 876 |     "list(range(20, 10, -2))"
 877 |    ]
 878 |   },
 879 |   {
 880 |    "cell_type": "markdown",
 881 |    "metadata": {},
 882 |    "source": [
 883 |     "###### 17. \n",
 884 |     "\n",
 885 |     "◑ Use `text9.index()` to find the index of the word *sunset*. You'll need to insert this word as an argument between the parentheses. By a process of trial and error, find the slice for the complete sentence that contains this word."
 886 |    ]
 887 |   },
 888 |   {
 889 |    "cell_type": "code",
 890 |    "execution_count": 100,
 891 |    "metadata": {},
 892 |    "outputs": [
 893 |     {
 894 |      "data": {
 895 |       "text/plain": [
 896 |        "629"
 897 |       ]
 898 |      },
 899 |      "execution_count": 100,
 900 |      "metadata": {},
 901 |      "output_type": "execute_result"
 902 |     }
 903 |    ],
 904 |    "source": [
 905 |     "text9.index('sunset')"
 906 |    ]
 907 |   },
 908 |   {
 909 |    "cell_type": "markdown",
 910 |    "metadata": {},
 911 |    "source": [
 912 |     "*We __could__ just use trial and error, but we could also use `for`-loops and conditionals to find the periods preceding and following the word `sunset`.*"
 913 |    ]
 914 |   },
 915 |   {
 916 |    "cell_type": "code",
 917 |    "execution_count": 118,
 918 |    "metadata": {},
 919 |    "outputs": [
 920 |     {
 921 |      "name": "stdout",
 922 |      "output_type": "stream",
 923 |      "text": [
 924 |       "612\n"
 925 |      ]
 926 |     }
 927 |    ],
 928 |    "source": [
 929 |     "for i in range(629, 600, -1):\n",
 930 |     "    if text9[i] == '.':\n",
 931 |     "        print(i)\n",
 932 |     "        break"
 933 |    ]
 934 |   },
 935 |   {
 936 |    "cell_type": "code",
 937 |    "execution_count": 119,
 938 |    "metadata": {},
 939 |    "outputs": [
 940 |     {
 941 |      "name": "stdout",
 942 |      "output_type": "stream",
 943 |      "text": [
 944 |       "643\n"
 945 |      ]
 946 |     }
 947 |    ],
 948 |    "source": [
 949 |     "for i in range(630, 650):\n",
 950 |     "    if text9[i] == '.':\n",
 951 |     "        print(i)\n",
 952 |     "        break"
 953 |    ]
 954 |   },
 955 |   {
 956 |    "cell_type": "markdown",
 957 |    "metadata": {},
 958 |    "source": [
 959 |     "*Simply printing the slice won't give us anything very legible:*"
 960 |    ]
 961 |   },
 962 |   {
 963 |    "cell_type": "code",
 964 |    "execution_count": 133,
 965 |    "metadata": {},
 966 |    "outputs": [
 967 |     {
 968 |      "name": "stdout",
 969 |      "output_type": "stream",
 970 |      "text": [
 971 |       "['CHAPTER', 'I', 'THE', 'TWO', 'POETS', 'OF', 'SAFFRON', 'PARK', 'THE', 'suburb', 'of', 'Saffron', 'Park', 'lay', 'on', 'the', 'sunset', 'side', 'of', 'London', ',', 'as', 'red', 'and', 'ragged', 'as', 'a', 'cloud', 'of', 'sunset', '.'] "
 972 |      ]
 973 |     }
 974 |    ],
 975 |    "source": [
 976 |     "print(text9[613:644], end = ' ')"
 977 |    ]
 978 |   },
 979 |   {
 980 |    "cell_type": "markdown",
 981 |    "metadata": {},
 982 |    "source": [
 983 |     "*But using `' '.join()` will add spaces before the punctuation:*"
 984 |    ]
 985 |   },
 986 |   {
 987 |    "cell_type": "code",
 988 |    "execution_count": 136,
 989 |    "metadata": {},
 990 |    "outputs": [
 991 |     {
 992 |      "data": {
 993 |       "text/plain": [
 994 |        "'CHAPTER I THE TWO POETS OF SAFFRON PARK THE suburb of Saffron Park lay on the sunset side of London , as red and ragged as a cloud of sunset .'"
 995 |       ]
 996 |      },
 997 |      "execution_count": 136,
 998 |      "metadata": {},
 999 |      "output_type": "execute_result"
1000 |     }
1001 |    ],
1002 |    "source": [
1003 |     "' '.join(text9[613:644])"
1004 |    ]
1005 |   },
1006 |   {
1007 |    "cell_type": "markdown",
1008 |    "metadata": {},
1009 |    "source": [
1010 |     "*Which we can fix with manual concatenation:*"
1011 |    ]
1012 |   },
1013 |   {
1014 |    "cell_type": "code",
1015 |    "execution_count": 131,
1016 |    "metadata": {},
1017 |    "outputs": [
1018 |     {
1019 |      "data": {
1020 |       "text/plain": [
1021 |        "'CHAPTER I THE TWO POETS OF SAFFRON PARK THE suburb of Saffron Park lay on the sunset side of London, as red and ragged as a cloud of sunset.'"
1022 |       ]
1023 |      },
1024 |      "execution_count": 131,
1025 |      "metadata": {},
1026 |      "output_type": "execute_result"
1027 |     }
1028 |    ],
1029 |    "source": [
1030 |     "' '.join(text9[613:633]) + text9[633] + ' ' + ' '.join(text9[634:643]) + text9[643]"
1031 |    ]
1032 |   },
1033 |   {
1034 |    "cell_type": "markdown",
1035 |    "metadata": {},
1036 |    "source": [
1037 |     "*Or, with a `for`-loop and a conditional that could be generalized for other sentences:* "
1038 |    ]
1039 |   },
1040 |   {
1041 |    "cell_type": "code",
1042 |    "execution_count": 135,
1043 |    "metadata": {},
1044 |    "outputs": [
1045 |     {
1046 |      "name": "stdout",
1047 |      "output_type": "stream",
1048 |      "text": [
1049 |       "CHAPTER I THE TWO POETS OF SAFFRON PARK THE suburb of Saffron Park lay on the sunset side of London, as red and ragged as a cloud of sunset. \n"
1050 |      ]
1051 |     }
1052 |    ],
1053 |    "source": [
1054 |     "full_sent = \"\"\n",
1055 |     "alpha_text = []\n",
1056 |     "\n",
1057 |     "for i in range(613, 644):\n",
1058 |     "    \n",
1059 |     "    if text9[i].isalpha():\n",
1060 |     "        alpha_text.append(text9[i])\n",
1061 |     "    else:\n",
1062 |     "        full_sent += (' '.join(alpha_text) + text9[i] + ' ')\n",
1063 |     "        alpha_text = []\n",
1064 |     "        \n",
1065 |     "print(full_sent)"
1066 |    ]
1067 |   },
1068 |   {
1069 |    "cell_type": "markdown",
1070 |    "metadata": {},
1071 |    "source": [
1072 |     "*I don't think `list.append` has been used in the book yet; but it's just a method to append an item to the end of a list.  You might think it's the same as just adding an item to a list; but the behavior is quite different.  Observe:*"
1073 |    ]
1074 |   },
1075 |   {
1076 |    "cell_type": "code",
1077 |    "execution_count": 334,
1078 |    "metadata": {},
1079 |    "outputs": [
1080 |     {
1081 |      "name": "stdout",
1082 |      "output_type": "stream",
1083 |      "text": [
1084 |       "C H A P T E R I T H E T W O P O E T S O F S A F F R O N P A R K T H E s u b u r b o f S a f f r o n P a r k l a y o n t h e s u n s e t s i d e o f L o n d o n, a s r e d a n d r a g g e d a s a c l o u d o f s u n s e t. \n"
1085 |      ]
1086 |     }
1087 |    ],
1088 |    "source": [
1089 |     "full_sent = \"\"\n",
1090 |     "alpha_text = []\n",
1091 |     "\n",
1092 |     "for i in range(613, 644):\n",
1093 |     "    \n",
1094 |     "    if text9[i].isalpha():\n",
1095 |     "        alpha_text += text9[i]\n",
1096 |     "    else:\n",
1097 |     "        full_sent += (' '.join(alpha_text) + text9[i] + ' ')\n",
1098 |     "        alpha_text = []\n",
1099 |     "        \n",
1100 |     "print(full_sent)"
1101 |    ]
1102 |   },
1103 |   {
1104 |    "cell_type": "markdown",
1105 |    "metadata": {},
1106 |    "source": [
1107 |     "###### 18.\n",
1108 |     "\n",
1109 |     "◑ Using list addition, and the `set` and `sorted` operations, compute the vocabulary of the sentences `sent1` ... `sent8`."
1110 |    ]
1111 |   },
1112 |   {
1113 |    "cell_type": "code",
1114 |    "execution_count": 145,
1115 |    "metadata": {},
1116 |    "outputs": [
1117 |     {
1118 |      "data": {
1119 |       "text/plain": [
1120 |        "75"
1121 |       ]
1122 |      },
1123 |      "execution_count": 145,
1124 |      "metadata": {},
1125 |      "output_type": "execute_result"
1126 |     }
1127 |    ],
1128 |    "source": [
1129 |     "len(set(sent1 + sent2 + sent3 + sent4 + sent5 + sent6 + sent7 + sent8))\n"
1130 |    ]
1131 |   },
1132 |   {
1133 |    "cell_type": "markdown",
1134 |    "metadata": {},
1135 |    "source": [
1136 |     "*Using `sorted` makes no difference to the number of types:*"
1137 |    ]
1138 |   },
1139 |   {
1140 |    "cell_type": "code",
1141 |    "execution_count": 146,
1142 |    "metadata": {},
1143 |    "outputs": [
1144 |     {
1145 |      "data": {
1146 |       "text/plain": [
1147 |        "75"
1148 |       ]
1149 |      },
1150 |      "execution_count": 146,
1151 |      "metadata": {},
1152 |      "output_type": "execute_result"
1153 |     }
1154 |    ],
1155 |    "source": [
1156 |     "len(sorted(set(sent1 + sent2 + sent3 + sent4 + sent5 + sent6 + sent7 + sent8)))\n"
1157 |    ]
1158 |   },
1159 |   {
1160 |    "cell_type": "markdown",
1161 |    "metadata": {},
1162 |    "source": [
1163 |     "###### 19.\n",
1164 |     "\n",
1165 |     "◑ What is the difference between the following two lines? Which one will give a larger value? Will this be the case for other texts?\n",
1166 |     "\n",
1167 |     "`sorted(set(w.lower() for w in text1))`\n",
1168 |     "`sorted(w.lower() for w in set(text1))`"
1169 |    ]
1170 |   },
1171 |   {
1172 |    "cell_type": "markdown",
1173 |    "metadata": {},
1174 |    "source": [
1175 |     "*The second line will give a larger value. It will treat lower- and uppercase versions of the same word as distinct strings and include both of them in the set.  The conversion to lowercase comes after this, so we'll ultimately end up with two lowercase versions of the same word.  This will be easier to observe with a toy example:*"
1176 |    ]
1177 |   },
1178 |   {
1179 |    "cell_type": "code",
1180 |    "execution_count": 152,
1181 |    "metadata": {},
1182 |    "outputs": [
1183 |     {
1184 |      "data": {
1185 |       "text/plain": [
1186 |        "['aye', 'captain']"
1187 |       ]
1188 |      },
1189 |      "execution_count": 152,
1190 |      "metadata": {},
1191 |      "output_type": "execute_result"
1192 |     }
1193 |    ],
1194 |    "source": [
1195 |     "test = [\"Aye\", \"aye\", \"captain\"]\n",
1196 |     "sorted(set(w.lower() for w in test))"
1197 |    ]
1198 |   },
1199 |   {
1200 |    "cell_type": "code",
1201 |    "execution_count": 153,
1202 |    "metadata": {},
1203 |    "outputs": [
1204 |     {
1205 |      "data": {
1206 |       "text/plain": [
1207 |        "['aye', 'aye', 'captain']"
1208 |       ]
1209 |      },
1210 |      "execution_count": 153,
1211 |      "metadata": {},
1212 |      "output_type": "execute_result"
1213 |     }
1214 |    ],
1215 |    "source": [
1216 |     "sorted(w.lower() for w in set(test))"
1217 |    ]
1218 |   },
1219 |   {
1220 |    "cell_type": "markdown",
1221 |    "metadata": {},
1222 |    "source": [
1223 |     "*Trying it with `text1`:*"
1224 |    ]
1225 |   },
1226 |   {
1227 |    "cell_type": "code",
1228 |    "execution_count": 155,
1229 |    "metadata": {},
1230 |    "outputs": [
1231 |     {
1232 |      "data": {
1233 |       "text/plain": [
1234 |        "17231"
1235 |       ]
1236 |      },
1237 |      "execution_count": 155,
1238 |      "metadata": {},
1239 |      "output_type": "execute_result"
1240 |     }
1241 |    ],
1242 |    "source": [
1243 |     "len(sorted(set(w.lower() for w in text1)))"
1244 |    ]
1245 |   },
1246 |   {
1247 |    "cell_type": "code",
1248 |    "execution_count": 156,
1249 |    "metadata": {},
1250 |    "outputs": [
1251 |     {
1252 |      "data": {
1253 |       "text/plain": [
1254 |        "19317"
1255 |       ]
1256 |      },
1257 |      "execution_count": 156,
1258 |      "metadata": {},
1259 |      "output_type": "execute_result"
1260 |     }
1261 |    ],
1262 |    "source": [
1263 |     "len(sorted(w.lower() for w in set(text1)))"
1264 |    ]
1265 |   },
1266 |   {
1267 |    "cell_type": "markdown",
1268 |    "metadata": {},
1269 |    "source": [
1270 |     "*For shits & giggles, trying it again with `text2`:*"
1271 |    ]
1272 |   },
1273 |   {
1274 |    "cell_type": "code",
1275 |    "execution_count": 157,
1276 |    "metadata": {},
1277 |    "outputs": [
1278 |     {
1279 |      "data": {
1280 |       "text/plain": [
1281 |        "6403"
1282 |       ]
1283 |      },
1284 |      "execution_count": 157,
1285 |      "metadata": {},
1286 |      "output_type": "execute_result"
1287 |     }
1288 |    ],
1289 |    "source": [
1290 |     "len(sorted(set(w.lower() for w in text2)))"
1291 |    ]
1292 |   },
1293 |   {
1294 |    "cell_type": "code",
1295 |    "execution_count": 158,
1296 |    "metadata": {},
1297 |    "outputs": [
1298 |     {
1299 |      "data": {
1300 |       "text/plain": [
1301 |        "6833"
1302 |       ]
1303 |      },
1304 |      "execution_count": 158,
1305 |      "metadata": {},
1306 |      "output_type": "execute_result"
1307 |     }
1308 |    ],
1309 |    "source": [
1310 |     "len(sorted(w.lower() for w in set(text2)))"
1311 |    ]
1312 |   },
1313 |   {
1314 |    "cell_type": "markdown",
1315 |    "metadata": {},
1316 |    "source": [
1317 |     "*The same would be true for any text with occurences of the same words in both lower- and uppercase.*"
1318 |    ]
1319 |   },
1320 |   {
1321 |    "cell_type": "markdown",
1322 |    "metadata": {},
1323 |    "source": [
1324 |     "###### 20.\n",
1325 |     "\n",
1326 |     "◑ What is the difference between the following two tests: `w.isupper()` and `not w.islower()`?\n",
1327 |     "\n",
1328 |     "*`not w.islower()` will return `True` on non-alphabetic characters:*"
1329 |    ]
1330 |   },
1331 |   {
1332 |    "cell_type": "code",
1333 |    "execution_count": 165,
1334 |    "metadata": {},
1335 |    "outputs": [
1336 |     {
1337 |      "data": {
1338 |       "text/plain": [
1339 |        "True"
1340 |       ]
1341 |      },
1342 |      "execution_count": 165,
1343 |      "metadata": {},
1344 |      "output_type": "execute_result"
1345 |     }
1346 |    ],
1347 |    "source": [
1348 |     "x = 'X'\n",
1349 |     "x.isupper()"
1350 |    ]
1351 |   },
1352 |   {
1353 |    "cell_type": "code",
1354 |    "execution_count": 166,
1355 |    "metadata": {},
1356 |    "outputs": [
1357 |     {
1358 |      "data": {
1359 |       "text/plain": [
1360 |        "True"
1361 |       ]
1362 |      },
1363 |      "execution_count": 166,
1364 |      "metadata": {},
1365 |      "output_type": "execute_result"
1366 |     }
1367 |    ],
1368 |    "source": [
1369 |     "not x.islower()"
1370 |    ]
1371 |   },
1372 |   {
1373 |    "cell_type": "code",
1374 |    "execution_count": 167,
1375 |    "metadata": {},
1376 |    "outputs": [
1377 |     {
1378 |      "data": {
1379 |       "text/plain": [
1380 |        "False"
1381 |       ]
1382 |      },
1383 |      "execution_count": 167,
1384 |      "metadata": {},
1385 |      "output_type": "execute_result"
1386 |     }
1387 |    ],
1388 |    "source": [
1389 |     "y = '!'\n",
1390 |     "y.isupper()"
1391 |    ]
1392 |   },
1393 |   {
1394 |    "cell_type": "code",
1395 |    "execution_count": 168,
1396 |    "metadata": {},
1397 |    "outputs": [
1398 |     {
1399 |      "data": {
1400 |       "text/plain": [
1401 |        "True"
1402 |       ]
1403 |      },
1404 |      "execution_count": 168,
1405 |      "metadata": {},
1406 |      "output_type": "execute_result"
1407 |     }
1408 |    ],
1409 |    "source": [
1410 |     "not y.islower()"
1411 |    ]
1412 |   },
1413 |   {
1414 |    "cell_type": "markdown",
1415 |    "metadata": {},
1416 |    "source": [
1417 |     "###### 21.\n",
1418 |     "\n",
1419 |     "◑ Write the slice expression that extracts the last two words of `text2`."
1420 |    ]
1421 |   },
1422 |   {
1423 |    "cell_type": "code",
1424 |    "execution_count": 169,
1425 |    "metadata": {},
1426 |    "outputs": [
1427 |     {
1428 |      "data": {
1429 |       "text/plain": [
1430 |        "['THE', 'END']"
1431 |       ]
1432 |      },
1433 |      "execution_count": 169,
1434 |      "metadata": {},
1435 |      "output_type": "execute_result"
1436 |     }
1437 |    ],
1438 |    "source": [
1439 |     "text2[-2:]"
1440 |    ]
1441 |   },
1442 |   {
1443 |    "cell_type": "markdown",
1444 |    "metadata": {},
1445 |    "source": [
1446 |     "###### 22.\n",
1447 |     "\n",
1448 |     "◑ Find all the four-letter words in the Chat Corpus (`text5`). With the help of a frequency distribution (`FreqDist`), show these words in decreasing order of frequency.\n",
1449 |     "\n",
1450 |     "*There are too many words in the distribution for us to display:*"
1451 |    ]
1452 |   },
1453 |   {
1454 |    "cell_type": "code",
1455 |    "execution_count": 170,
1456 |    "metadata": {},
1457 |    "outputs": [],
1458 |    "source": [
1459 |     "four_letter_fd = FreqDist([w for w in text5 if len(w) == 4])"
1460 |    ]
1461 |   },
1462 |   {
1463 |    "cell_type": "code",
1464 |    "execution_count": 181,
1465 |    "metadata": {},
1466 |    "outputs": [
1467 |     {
1468 |      "data": {
1469 |       "text/plain": [
1470 |        "1181"
1471 |       ]
1472 |      },
1473 |      "execution_count": 181,
1474 |      "metadata": {},
1475 |      "output_type": "execute_result"
1476 |     }
1477 |    ],
1478 |    "source": [
1479 |     "len(four_letter_fd)"
1480 |    ]
1481 |   },
1482 |   {
1483 |    "cell_type": "markdown",
1484 |    "metadata": {},
1485 |    "source": [
1486 |     "*So we'll just print the first hundred:*"
1487 |    ]
1488 |   },
1489 |   {
1490 |    "cell_type": "code",
1491 |    "execution_count": 180,
1492 |    "metadata": {},
1493 |    "outputs": [
1494 |     {
1495 |      "name": "stdout",
1496 |      "output_type": "stream",
1497 |      "text": [
1498 |       "[('JOIN', 1021), ('PART', 1016), ('that', 274), ('what', 183), ('here', 181), ('....', 170), ('have', 164), ('like', 156), ('with', 152), ('chat', 142), ('your', 137), ('good', 130), ('just', 125), ('lmao', 107), ('know', 103), ('room', 98), ('from', 92), ('this', 86), ('well', 81), ('back', 78), ('hiya', 78), ('they', 77), ('dont', 75), ('yeah', 75), ('want', 71), ('love', 60), ('guys', 58), ('some', 58), ('been', 57), ('talk', 56), ('nice', 52), ('time', 50), ('when', 48), ('haha', 44), ('make', 44), ('girl', 43), ('need', 43), ('U122', 42), ('MODE', 41), ('will', 40), ('much', 40), ('then', 40), ('over', 39), ('work', 38), ('were', 38), ('take', 37), ('U121', 36), ('U115', 36), ('song', 36), ('even', 35), ('does', 35), ('seen', 35), ('U156', 35), ('U105', 35), ('more', 34), ('damn', 34), ('only', 33), ('come', 33), ('hell', 29), ('long', 28), ('them', 28), ('name', 27), ('tell', 27), ('away', 26), ('sure', 26), ('look', 26), ('baby', 26), ('call', 26), ('play', 25), ('U110', 25), ('U114', 25), ('NICK', 24), ('down', 24), ('cool', 24), ('sexy', 23), ('many', 23), ('hate', 23), ('said', 23), ('last', 22), ('ever', 22), ('hear', 21), ('life', 21), ('live', 20), ('feel', 19), ('very', 19), ('mean', 19), ('give', 19), ('same', 19), ('must', 19), ('stop', 19), ('LMAO', 19), ('!!!!', 18), ('hugs', 18), ('What', 18), ('find', 18), ('cant', 18), ('left', 17), ('????', 17), ('shit', 17), ('nite', 17)]"
1499 |      ]
1500 |     }
1501 |    ],
1502 |    "source": [
1503 |     "print(four_letter_fd.most_common(100), end = '')"
1504 |    ]
1505 |   },
1506 |   {
1507 |    "cell_type": "markdown",
1508 |    "metadata": {},
1509 |    "source": [
1510 |     "*However, it's probably advisable to ignore case:*"
1511 |    ]
1512 |   },
1513 |   {
1514 |    "cell_type": "code",
1515 |    "execution_count": 182,
1516 |    "metadata": {},
1517 |    "outputs": [
1518 |     {
1519 |      "name": "stdout",
1520 |      "output_type": "stream",
1521 |      "text": [
1522 |       "[('part', 1022), ('join', 1021), ('that', 284), ('what', 201), ('here', 185), ('have', 171), ('....', 170), ('like', 160), ('with', 154), ('chat', 146), ('your', 142), ('good', 132), ('lmao', 128), ('just', 128), ('know', 104), ('room', 103), ('this', 98), ('from', 96), ('well', 91), ('yeah', 85), ('hiya', 85), ('they', 84), ('back', 79), ('dont', 77), ('want', 71), ('love', 63), ('guys', 59), ('some', 59), ('talk', 58), ('been', 58), ('nice', 54), ('when', 54), ('time', 52), ('haha', 46), ('girl', 45), ('make', 44), ('need', 44), ('will', 42), ('song', 42), ('mode', 42), ('u122', 42), ('then', 41), ('much', 40), ('over', 40), ('were', 39), ('take', 38), ('does', 38), ('work', 38), ('even', 37), ('u121', 36), ('u115', 36), ('only', 36), ('damn', 36), ('seen', 36), ('come', 36), ('more', 35), ('u156', 35), ('u105', 35), ('nick', 33), ('long', 30), ('hell', 29), ('last', 29), ('tell', 29), ('name', 28), ('away', 28), ('sure', 28), ('them', 28), ('look', 27), ('baby', 26), ('call', 26), ('down', 26), ('cool', 26), ('sexy', 25), ('play', 25), ('u110', 25), ('u114', 25), ('many', 23), ('hate', 23), ('said', 23), ('same', 23), ('live', 23), ('stop', 23), ('life', 22), ('ever', 22), ('hear', 21), ('very', 20), ('feel', 19), ('hugs', 19), ('mean', 19), ('give', 19), ('must', 19), ('!!!!', 18), ('find', 18), ('cant', 18), ('left', 17), ('fine', 17), ('lets', 17), ('????', 17), ('shit', 17), ('nite', 17)]"
1523 |      ]
1524 |     }
1525 |    ],
1526 |    "source": [
1527 |     "four_letter_fd = FreqDist(w.lower() for w in text5 if len(w) == 4)\n",
1528 |     "print(four_letter_fd.most_common(100), end = '')"
1529 |    ]
1530 |   },
1531 |   {
1532 |    "cell_type": "markdown",
1533 |    "metadata": {},
1534 |    "source": [
1535 |     "*This would be easier to analyze as a plot:*"
1536 |    ]
1537 |   },
1538 |   {
1539 |    "cell_type": "code",
1540 |    "execution_count": 190,
1541 |    "metadata": {},
1542 |    "outputs": [
1543 |     {
1544 |      "data": {
1545 |       "image/png": 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\n",
1546 |       "text/plain": [
1547 |        "<Figure size 432x288 with 1 Axes>"
1548 |       ]
1549 |      },
1550 |      "metadata": {
1551 |       "needs_background": "light"
1552 |      },
1553 |      "output_type": "display_data"
1554 |     }
1555 |    ],
1556 |    "source": [
1557 |     "four_letter_fd.plot(50);"
1558 |    ]
1559 |   },
1560 |   {
1561 |    "cell_type": "markdown",
1562 |    "metadata": {},
1563 |    "source": [
1564 |     "###### 23.\n",
1565 |     "\n",
1566 |     "◑ Review the discussion of looping with conditions in [4](https://www.nltk.org/book/ch01.html#sec-making-decisions \"Looping with conditions\"). Use a combination of `for` and `if` statements to loop over the words of the movie script for *Monty Python and the Holy Grail* (`text6`) and `print` all the uppercase words, one per line.\n",
1567 |     "\n",
1568 |     "*To save space in this notebook, I'm only going to consider the first 300 words in the script.  To examine the whole script, just delete this index.*"
1569 |    ]
1570 |   },
1571 |   {
1572 |    "cell_type": "code",
1573 |    "execution_count": 199,
1574 |    "metadata": {},
1575 |    "outputs": [
1576 |     {
1577 |      "name": "stdout",
1578 |      "output_type": "stream",
1579 |      "text": [
1580 |       "SCENE\n",
1581 |       " KING\n",
1582 |       " ARTHUR\n",
1583 |       " SOLDIER\n",
1584 |       " ARTHUR\n",
1585 |       " I\n",
1586 |       " SOLDIER\n",
1587 |       " ARTHUR\n",
1588 |       " I\n",
1589 |       " I\n",
1590 |       " SOLDIER\n",
1591 |       " ARTHUR\n",
1592 |       " SOLDIER\n",
1593 |       " ARTHUR\n",
1594 |       " SOLDIER\n",
1595 |       " ARTHUR\n",
1596 |       " SOLDIER\n",
1597 |       " ARTHUR\n",
1598 |       " SOLDIER\n",
1599 |       " ARTHUR\n",
1600 |       " SOLDIER\n",
1601 |       " ARTHUR\n",
1602 |       " SOLDIER\n",
1603 |       " ARTHUR\n",
1604 |       " "
1605 |      ]
1606 |     }
1607 |    ],
1608 |    "source": [
1609 |     "for w in text6[:300]:\n",
1610 |     "    if w.isupper():\n",
1611 |     "        print(w, end = '\\n ')"
1612 |    ]
1613 |   },
1614 |   {
1615 |    "cell_type": "markdown",
1616 |    "metadata": {},
1617 |    "source": [
1618 |     "<i>If we preferred to use a list comprehension, we'd have to use `*` to unpack the list, and `sep` instead of `end`.</i>"
1619 |    ]
1620 |   },
1621 |   {
1622 |    "cell_type": "code",
1623 |    "execution_count": 219,
1624 |    "metadata": {},
1625 |    "outputs": [
1626 |     {
1627 |      "name": "stdout",
1628 |      "output_type": "stream",
1629 |      "text": [
1630 |       "SCENE\n",
1631 |       "KING\n",
1632 |       "ARTHUR\n",
1633 |       "SOLDIER\n",
1634 |       "ARTHUR\n",
1635 |       "I\n",
1636 |       "SOLDIER\n",
1637 |       "ARTHUR\n",
1638 |       "I\n",
1639 |       "I\n",
1640 |       "SOLDIER\n",
1641 |       "ARTHUR\n",
1642 |       "SOLDIER\n",
1643 |       "ARTHUR\n",
1644 |       "SOLDIER\n",
1645 |       "ARTHUR\n",
1646 |       "SOLDIER\n",
1647 |       "ARTHUR\n",
1648 |       "SOLDIER\n",
1649 |       "ARTHUR\n",
1650 |       "SOLDIER\n",
1651 |       "ARTHUR\n",
1652 |       "SOLDIER\n",
1653 |       "ARTHUR\n"
1654 |      ]
1655 |     }
1656 |    ],
1657 |    "source": [
1658 |     "print(*[w for w in text6[:300] if w.isupper()], sep = \"\\n\")"
1659 |    ]
1660 |   },
1661 |   {
1662 |    "cell_type": "markdown",
1663 |    "metadata": {},
1664 |    "source": [
1665 |     "###### 24.\n",
1666 |     "\n",
1667 |     "◑ Write expressions for finding all words in `text6` that meet the conditions listed below. The result should be in the form of a list of words: [`'word1', 'word2', ...`].\n",
1668 |     "\n",
1669 |     "+ a. Ending in *ise*\n",
1670 |     "    \n",
1671 |     "+ b. Containing the letter *z*\n",
1672 |     "    \n",
1673 |     "+ c. Containing the sequence of letters *pt*\n",
1674 |     "    \n",
1675 |     "+ d. Having all lowercase letters except for an initial capital (i.e., `titlecase`)\n",
1676 |     "    \n",
1677 |     "*The most basic way of doing this is to use a series of `if...elif` conditionals for each condition.  One thing we need to watch out for is the fact that we'd need to find occurences of strings with both lower- and uppercase `z`s (as well as `pt`s).  For all practical purposes, it's not a huge concern: the `w.istitle()` conditional will find any words that begin with `Z`.  However, `w.istitle()` would miss words in all caps (e.g., `ZZZZ`) or in camelcase (e.g., `tHIS`), etc... So we'll use the conditional `if 'z' in w.lower()` to handle this.  The conditional will change the word to lowercase for the check, but pass the unaltered form to the list, so the words in the list will appear as they did in the text.*"
1678 |    ]
1679 |   },
1680 |   {
1681 |    "cell_type": "code",
1682 |    "execution_count": 244,
1683 |    "metadata": {},
1684 |    "outputs": [
1685 |     {
1686 |      "name": "stdout",
1687 |      "output_type": "stream",
1688 |      "text": [
1689 |       "['A', 'AMAZING', 'Aaaaaaaaah', 'Aaaaaaaah', 'Aaaaaah', 'Aaaah', 'Aaaaugh', 'Aaagh', 'Aaah', 'Aaauggh', 'Aaaugh', 'Aaauugh', 'Aagh', 'Aah', 'Aauuggghhh', 'Aauuugh', 'Aauuuuugh', 'Aauuuves', 'Action', 'Actually', 'African', 'Ages', 'Aggh', 'Agh', 'Ah', 'Ahh', 'Alice', 'All', 'Allo', 'Almighty', 'Alright', 'Am', 'Amen', 'An', 'Anarcho', 'And', 'Angnor', 'Anthrax', 'Antioch', 'Anybody', 'Anyway', 'Apples', 'Aramaic', 'Are', 'Arimathea', 'Armaments', 'Arthur', 'As', 'Ask', 'Assyria', 'At', 'Attila', 'Augh', 'Autumn', 'Auuuuuuuugh', 'Away', 'Ay', 'Ayy', 'B', 'Back', 'Bad', 'Badon', 'Battle', 'Be', 'Beast', 'Bedevere', 'Bedwere', 'Behold', 'Between', 'Beyond', 'Black', 'Bloody', 'Blue', 'Bon', 'Bones', 'Book', 'Bors', 'Brave', 'Bravely', 'Bravest', 'Bread', 'Bridge', 'Bring', 'Bristol', 'Britain', 'Britons', 'Brother', 'Build', 'Burn', 'But', 'By', 'C', 'Caerbannog', 'Camaaaaaargue', 'Camelot', 'Castle', 'Chapter', 'Charge', 'Chaste', 'Cherries', 'Chicken', 'Chickennn', 'Chop', 'Christ', 'Churches', 'Cider', 'Clark', 'Clear', 'Come', 'Concorde', 'Consult', 'Cornwall', 'Could', 'Course', 'Court', 'Crapper', 'Cut', 'Dappy', 'Death', 'Defeat', 'Dennis', 'Did', 'Didn', 'Dingo', 'Dis', 'Divine', 'Do', 'Doctor', 'Does', 'Don', 'Dragon', 'Dramatically', 'Ecky', 'Ector', 'Eee', 'Eh', 'Enchanter', 'England', 'English', 'Erbert', 'Ere', 'Erm', 'Eternal', 'European', 'Even', 'Every', 'Everything', 'Ewing', 'Exactly', 'Excalibur', 'Excuse', 'Explain', 'Far', 'Farewell', 'Father', 'Fetchez', 'Fiends', 'Fine', 'First', 'Firstly', 'Five', 'Follow', 'For', 'Forgive', 'Forward', 'Found', 'Four', 'France', 'Frank', 'French', 'Gable', 'Galahad', 'Gallahad', 'Gawain', 'Get', 'Go', 'God', 'Good', 'Gorge', 'Grail', 'Great', 'Greetings', 'Grenade', 'Guards', 'Guy', 'Ha', 'Hah', 'Hallo', 'Halt', 'Hand', 'Hang', 'Have', 'Haw', 'He', 'Hee', 'Heee', 'Heh', 'Hello', 'Help', 'Herbert', 'Here', 'Hey', 'Hic', 'Hill', 'Himself', 'His', 'Hiyaah', 'Hiyah', 'Hiyya', 'Hm', 'Hmm', 'Ho', 'Hoa', 'Hold', 'Holy', 'Honestly', 'Hoo', 'Hooray', 'How', 'Huh', 'Hurry', 'Huy', 'Huyah', 'Hya', 'Hyy', 'I', 'Idiom', 'Iesu', 'If', 'Iiiiives', 'Iiiives', 'In', 'Is', 'Isn', 'It', 'Ives', 'Jesus', 'Joseph', 'Just', 'Keep', 'King', 'Knight', 'Knights', 'Lady', 'Lake', 'Lancelot', 'Launcelot', 'Lead', 'Leaving', 'Let', 'Lie', 'Like', 'Listen', 'Loimbard', 'Look', 'Looks', 'Lord', 'Lucky', 'Make', 'Man', 'May', 'Maynard', 'Meanwhile', 'Mercea', 'Message', 'Midget', 'Mind', 'Mine', 'Mmm', 'Monsieur', 'More', 'Morning', 'Most', 'Mother', 'Mud', 'Must', 'My', 'N', 'Nador', 'Nay', 'Neee', 'Never', 'Ni', 'Nine', 'Ninepence', 'No', 'None', 'Not', 'Nothing', 'Now', 'Nu', 'O', 'Of', 'Off', 'Oh', 'Ohh', 'Old', 'Olfin', 'On', 'Once', 'One', 'Ooh', 'Oooh', 'Oooo', 'Oooohoohohooo', 'Oooooooh', 'Open', 'Or', 'Order', 'Other', 'Oui', 'Our', 'Over', 'Ow', 'Packing', 'Patsy', 'Pendragon', 'Peng', 'Perhaps', 'Peril', 'Picture', 'Pie', 'Piglet', 'Pin', 'Please', 'Practice', 'Prepare', 'Prince', 'Princess', 'Providence', 'Psalms', 'Pull', 'Pure', 'Put', 'Quick', 'Quickly', 'Quiet', 'Quite', 'Quoi', 'Rather', 'Really', 'Recently', 'Remove', 'Rheged', 'Ridden', 'Right', 'Riiight', 'Robin', 'Robinson', 'Roger', 'Round', 'Run', 'Running', 'S', 'Said', 'Saint', 'Saxons', 'Say', 'Schools', 'See', 'Seek', 'Shall', 'She', 'Shh', 'Shrubber', 'Shrubberies', 'Shut', 'Silence', 'Silly', 'Since', 'Sir', 'Skip', 'So', 'Sorry', 'Speak', 'Splendid', 'Spring', 'Stand', 'Stay', 'Steady', 'Stop', 'Summer', 'Supposing', 'Supreme', 'Surely', 'Swamp', 'Table', 'Tale', 'Tall', 'Tell', 'Thank', 'That', 'The', 'Thee', 'Then', 'There', 'Therefore', 'They', 'This', 'Those', 'Thou', 'Thpppppt', 'Thppppt', 'Thpppt', 'Thppt', 'Three', 'Throw', 'Thsss', 'Thursday', 'Thy', 'Til', 'Tim', 'Tis', 'To', 'Today', 'Together', 'Too', 'Torment', 'Tower', 'True', 'Try', 'Twenty', 'Two', 'U', 'Uh', 'Uhh', 'Ulk', 'Um', 'Umhm', 'Umm', 'Un', 'Unfortunately', 'Until', 'Use', 'Uther', 'Uugh', 'Uuh', 'Very', 'Victory', 'W', 'Waa', 'Wait', 'Walk', 'Wayy', 'We', 'Welcome', 'Well', 'What', 'When', 'Where', 'Which', 'Who', 'Whoa', 'Why', 'Will', 'Winston', 'Winter', 'With', 'Woa', 'Wood', 'Would', 'Y', 'Yapping', 'Yay', 'Yeaaah', 'Yeaah', 'Yeah', 'Yes', 'You', 'Your', 'Yup', 'ZOOT', 'Zoot', 'amazes', 'apologise', 'aptly', 'empty', 'excepting', 'frozen', 'noise', 'ptoo', 'surprise', 'temptation', 'temptress', 'wise', 'zhiv', 'zone', 'zoo', 'zoop', 'zoosh']\n"
1690 |      ]
1691 |     }
1692 |    ],
1693 |    "source": [
1694 |     "my_list = []\n",
1695 |     "\n",
1696 |     "for w in text6:\n",
1697 |     "    if w.endswith('ise'):\n",
1698 |     "        my_list.append(w)\n",
1699 |     "    elif 'z' in w.lower():\n",
1700 |     "        my_list.append(w)\n",
1701 |     "    elif 'pt' in w.lower():\n",
1702 |     "        my_list.append(w)\n",
1703 |     "    elif w.istitle():\n",
1704 |     "        my_list.append(w)\n",
1705 |     "        \n",
1706 |     "\n",
1707 |     "print(sorted(set(my_list)))"
1708 |    ]
1709 |   },
1710 |   {
1711 |    "cell_type": "markdown",
1712 |    "metadata": {},
1713 |    "source": [
1714 |     "*There are two ways we could make the above conditional more concise: 1) use a list comprehension with `or` to connect the multiple conditionals; 2) use `any` with an additional list comprehension so we can find words with either `pt` or `z` in the same conditional.*"
1715 |    ]
1716 |   },
1717 |   {
1718 |    "cell_type": "code",
1719 |    "execution_count": 245,
1720 |    "metadata": {},
1721 |    "outputs": [
1722 |     {
1723 |      "name": "stdout",
1724 |      "output_type": "stream",
1725 |      "text": [
1726 |       "['A', 'AMAZING', 'Aaaaaaaaah', 'Aaaaaaaah', 'Aaaaaah', 'Aaaah', 'Aaaaugh', 'Aaagh', 'Aaah', 'Aaauggh', 'Aaaugh', 'Aaauugh', 'Aagh', 'Aah', 'Aauuggghhh', 'Aauuugh', 'Aauuuuugh', 'Aauuuves', 'Action', 'Actually', 'African', 'Ages', 'Aggh', 'Agh', 'Ah', 'Ahh', 'Alice', 'All', 'Allo', 'Almighty', 'Alright', 'Am', 'Amen', 'An', 'Anarcho', 'And', 'Angnor', 'Anthrax', 'Antioch', 'Anybody', 'Anyway', 'Apples', 'Aramaic', 'Are', 'Arimathea', 'Armaments', 'Arthur', 'As', 'Ask', 'Assyria', 'At', 'Attila', 'Augh', 'Autumn', 'Auuuuuuuugh', 'Away', 'Ay', 'Ayy', 'B', 'Back', 'Bad', 'Badon', 'Battle', 'Be', 'Beast', 'Bedevere', 'Bedwere', 'Behold', 'Between', 'Beyond', 'Black', 'Bloody', 'Blue', 'Bon', 'Bones', 'Book', 'Bors', 'Brave', 'Bravely', 'Bravest', 'Bread', 'Bridge', 'Bring', 'Bristol', 'Britain', 'Britons', 'Brother', 'Build', 'Burn', 'But', 'By', 'C', 'Caerbannog', 'Camaaaaaargue', 'Camelot', 'Castle', 'Chapter', 'Charge', 'Chaste', 'Cherries', 'Chicken', 'Chickennn', 'Chop', 'Christ', 'Churches', 'Cider', 'Clark', 'Clear', 'Come', 'Concorde', 'Consult', 'Cornwall', 'Could', 'Course', 'Court', 'Crapper', 'Cut', 'Dappy', 'Death', 'Defeat', 'Dennis', 'Did', 'Didn', 'Dingo', 'Dis', 'Divine', 'Do', 'Doctor', 'Does', 'Don', 'Dragon', 'Dramatically', 'Ecky', 'Ector', 'Eee', 'Eh', 'Enchanter', 'England', 'English', 'Erbert', 'Ere', 'Erm', 'Eternal', 'European', 'Even', 'Every', 'Everything', 'Ewing', 'Exactly', 'Excalibur', 'Excuse', 'Explain', 'Far', 'Farewell', 'Father', 'Fetchez', 'Fiends', 'Fine', 'First', 'Firstly', 'Five', 'Follow', 'For', 'Forgive', 'Forward', 'Found', 'Four', 'France', 'Frank', 'French', 'Gable', 'Galahad', 'Gallahad', 'Gawain', 'Get', 'Go', 'God', 'Good', 'Gorge', 'Grail', 'Great', 'Greetings', 'Grenade', 'Guards', 'Guy', 'Ha', 'Hah', 'Hallo', 'Halt', 'Hand', 'Hang', 'Have', 'Haw', 'He', 'Hee', 'Heee', 'Heh', 'Hello', 'Help', 'Herbert', 'Here', 'Hey', 'Hic', 'Hill', 'Himself', 'His', 'Hiyaah', 'Hiyah', 'Hiyya', 'Hm', 'Hmm', 'Ho', 'Hoa', 'Hold', 'Holy', 'Honestly', 'Hoo', 'Hooray', 'How', 'Huh', 'Hurry', 'Huy', 'Huyah', 'Hya', 'Hyy', 'I', 'Idiom', 'Iesu', 'If', 'Iiiiives', 'Iiiives', 'In', 'Is', 'Isn', 'It', 'Ives', 'Jesus', 'Joseph', 'Just', 'Keep', 'King', 'Knight', 'Knights', 'Lady', 'Lake', 'Lancelot', 'Launcelot', 'Lead', 'Leaving', 'Let', 'Lie', 'Like', 'Listen', 'Loimbard', 'Look', 'Looks', 'Lord', 'Lucky', 'Make', 'Man', 'May', 'Maynard', 'Meanwhile', 'Mercea', 'Message', 'Midget', 'Mind', 'Mine', 'Mmm', 'Monsieur', 'More', 'Morning', 'Most', 'Mother', 'Mud', 'Must', 'My', 'N', 'Nador', 'Nay', 'Neee', 'Never', 'Ni', 'Nine', 'Ninepence', 'No', 'None', 'Not', 'Nothing', 'Now', 'Nu', 'O', 'Of', 'Off', 'Oh', 'Ohh', 'Old', 'Olfin', 'On', 'Once', 'One', 'Ooh', 'Oooh', 'Oooo', 'Oooohoohohooo', 'Oooooooh', 'Open', 'Or', 'Order', 'Other', 'Oui', 'Our', 'Over', 'Ow', 'Packing', 'Patsy', 'Pendragon', 'Peng', 'Perhaps', 'Peril', 'Picture', 'Pie', 'Piglet', 'Pin', 'Please', 'Practice', 'Prepare', 'Prince', 'Princess', 'Providence', 'Psalms', 'Pull', 'Pure', 'Put', 'Quick', 'Quickly', 'Quiet', 'Quite', 'Quoi', 'Rather', 'Really', 'Recently', 'Remove', 'Rheged', 'Ridden', 'Right', 'Riiight', 'Robin', 'Robinson', 'Roger', 'Round', 'Run', 'Running', 'S', 'Said', 'Saint', 'Saxons', 'Say', 'Schools', 'See', 'Seek', 'Shall', 'She', 'Shh', 'Shrubber', 'Shrubberies', 'Shut', 'Silence', 'Silly', 'Since', 'Sir', 'Skip', 'So', 'Sorry', 'Speak', 'Splendid', 'Spring', 'Stand', 'Stay', 'Steady', 'Stop', 'Summer', 'Supposing', 'Supreme', 'Surely', 'Swamp', 'Table', 'Tale', 'Tall', 'Tell', 'Thank', 'That', 'The', 'Thee', 'Then', 'There', 'Therefore', 'They', 'This', 'Those', 'Thou', 'Thpppppt', 'Thppppt', 'Thpppt', 'Thppt', 'Three', 'Throw', 'Thsss', 'Thursday', 'Thy', 'Til', 'Tim', 'Tis', 'To', 'Today', 'Together', 'Too', 'Torment', 'Tower', 'True', 'Try', 'Twenty', 'Two', 'U', 'Uh', 'Uhh', 'Ulk', 'Um', 'Umhm', 'Umm', 'Un', 'Unfortunately', 'Until', 'Use', 'Uther', 'Uugh', 'Uuh', 'Very', 'Victory', 'W', 'Waa', 'Wait', 'Walk', 'Wayy', 'We', 'Welcome', 'Well', 'What', 'When', 'Where', 'Which', 'Who', 'Whoa', 'Why', 'Will', 'Winston', 'Winter', 'With', 'Woa', 'Wood', 'Would', 'Y', 'Yapping', 'Yay', 'Yeaaah', 'Yeaah', 'Yeah', 'Yes', 'You', 'Your', 'Yup', 'ZOOT', 'Zoot', 'amazes', 'apologise', 'aptly', 'empty', 'excepting', 'frozen', 'noise', 'ptoo', 'surprise', 'temptation', 'temptress', 'wise', 'zhiv', 'zone', 'zoo', 'zoop', 'zoosh']\n"
1727 |      ]
1728 |     }
1729 |    ],
1730 |    "source": [
1731 |     "print(sorted(set([w for w in text6 if w.endswith('ise') or any(s in w.lower() for s in ('pt', 'z')) or w.istitle()])))"
1732 |    ]
1733 |   },
1734 |   {
1735 |    "cell_type": "markdown",
1736 |    "metadata": {},
1737 |    "source": [
1738 |     "###### 25. \n",
1739 |     "◑ Define `sent` to be the list of words `['she', 'sells', 'sea', 'shells', 'by', 'the', 'sea', 'shore']`. Now write code to perform the following tasks:\n",
1740 |     "\n",
1741 |     "+ a. Print all words beginning with *sh*\n",
1742 |     "    \n",
1743 |     "+ b. Print all words longer than four characters"
1744 |    ]
1745 |   },
1746 |   {
1747 |    "cell_type": "code",
1748 |    "execution_count": 246,
1749 |    "metadata": {},
1750 |    "outputs": [
1751 |     {
1752 |      "name": "stdout",
1753 |      "output_type": "stream",
1754 |      "text": [
1755 |       "['she', 'shells', 'shore']\n"
1756 |      ]
1757 |     }
1758 |    ],
1759 |    "source": [
1760 |     "sent = ['she', 'sells', 'sea', 'shells', 'by', 'the', 'sea', 'shore']\n",
1761 |     "\n",
1762 |     "print([w for w in sent if w.startswith('sh')])"
1763 |    ]
1764 |   },
1765 |   {
1766 |    "cell_type": "code",
1767 |    "execution_count": 247,
1768 |    "metadata": {},
1769 |    "outputs": [
1770 |     {
1771 |      "name": "stdout",
1772 |      "output_type": "stream",
1773 |      "text": [
1774 |       "['sells', 'shells', 'shore']\n"
1775 |      ]
1776 |     }
1777 |    ],
1778 |    "source": [
1779 |     "print([w for w in sent if len(w) > 4])"
1780 |    ]
1781 |   },
1782 |   {
1783 |    "cell_type": "markdown",
1784 |    "metadata": {},
1785 |    "source": [
1786 |     "###### 26. \n",
1787 |     "\n",
1788 |     "◑ What does the following Python code do? `sum(len(w) for w in text1)` Can you use it to work out the average word length of a text?\n",
1789 |     "\n",
1790 |     "*`sum(len(w) for w in text1)` will give us the number of characters in `text1`.  Dividing this sum by the length of `text1` will give us a rough idea of the average word length of the text.  However, this number will not be entirely accurate, since blocks of punctuation will be considered types:*"
1791 |    ]
1792 |   },
1793 |   {
1794 |    "cell_type": "code",
1795 |    "execution_count": 266,
1796 |    "metadata": {},
1797 |    "outputs": [
1798 |     {
1799 |      "name": "stdout",
1800 |      "output_type": "stream",
1801 |      "text": [
1802 |       "Total number of characters: 999044\n"
1803 |      ]
1804 |     }
1805 |    ],
1806 |    "source": [
1807 |     "tot_chars = sum(len(w) for w in text1)\n",
1808 |     "\n",
1809 |     "print(\"Total number of characters: {}\".format(tot_chars))"
1810 |    ]
1811 |   },
1812 |   {
1813 |    "cell_type": "markdown",
1814 |    "metadata": {},
1815 |    "source": [
1816 |     "*Average word length:*"
1817 |    ]
1818 |   },
1819 |   {
1820 |    "cell_type": "code",
1821 |    "execution_count": 256,
1822 |    "metadata": {},
1823 |    "outputs": [
1824 |     {
1825 |      "data": {
1826 |       "text/plain": [
1827 |        "3.830411128023649"
1828 |       ]
1829 |      },
1830 |      "execution_count": 256,
1831 |      "metadata": {},
1832 |      "output_type": "execute_result"
1833 |     }
1834 |    ],
1835 |    "source": [
1836 |     "sum(len(w) for w in text1)/len(text1)"
1837 |    ]
1838 |   },
1839 |   {
1840 |    "cell_type": "markdown",
1841 |    "metadata": {},
1842 |    "source": [
1843 |     "*If we perform similar calculations on non-alphanumeric characters, we can see that they comprise nearly 5% of the characters in the text, which will affect our derivation of the average word length:*"
1844 |    ]
1845 |   },
1846 |   {
1847 |    "cell_type": "code",
1848 |    "execution_count": 301,
1849 |    "metadata": {},
1850 |    "outputs": [
1851 |     {
1852 |      "name": "stdout",
1853 |      "output_type": "stream",
1854 |      "text": [
1855 |       "Total number of non-alphanumeric characters: 46415\n",
1856 |       "Percentage of non-alphanumeric characters: 4.645942%\n"
1857 |      ]
1858 |     }
1859 |    ],
1860 |    "source": [
1861 |     "tot_na_chars = sum(len(w) for w in text1 if not w.isalnum())\n",
1862 |     "perc_na_chars = tot_na_chars/tot_chars\n",
1863 |     "\n",
1864 |     "print(\"Total number of non-alphanumeric characters: {}\".format(tot_na_chars))\n",
1865 |     "print(\"Percentage of non-alphanumeric characters: {:%}\".format(perc_na_chars))"
1866 |    ]
1867 |   },
1868 |   {
1869 |    "cell_type": "markdown",
1870 |    "metadata": {},
1871 |    "source": [
1872 |     "*This would be the word length average if we eliminated non-alphabetic characters:*"
1873 |    ]
1874 |   },
1875 |   {
1876 |    "cell_type": "code",
1877 |    "execution_count": 299,
1878 |    "metadata": {},
1879 |    "outputs": [
1880 |     {
1881 |      "data": {
1882 |       "text/plain": [
1883 |        "4.35945521407211"
1884 |       ]
1885 |      },
1886 |      "execution_count": 299,
1887 |      "metadata": {},
1888 |      "output_type": "execute_result"
1889 |     }
1890 |    ],
1891 |    "source": [
1892 |     "sum(len(w) for w in text1 if w.isalpha())/len([w for w in text1 if w.isalpha()])"
1893 |    ]
1894 |   },
1895 |   {
1896 |    "cell_type": "markdown",
1897 |    "metadata": {},
1898 |    "source": [
1899 |     "*4.36 vs. 3.83 is a sizeable difference.*\n",
1900 |     "\n",
1901 |     "*A further consideration is how we'd handle words with apostrophes.  Currently, these words are split into three parts: the main part, the apostrophe, and the enclitic (e.g., `didn't` => `didn`, `'`, `t`).  Even if we remove the apostrophes from the final counts, we'd still have the problem of enclitics (i.e., \"d\", \"ll\", \"m\", \"re\", \"s\", \"t\", and \"ve\") - single/double letters that would be counted as individual words, which would drive the average word length down.  I'd argue that these endings should be rejoined to the words they were originally a part of. There are some complicated ways of dealing with this (e.g., going through the text and reattaching these enclitics); but a simpler solution would just be to leave these endings in the counts of total characters, but remove them from the counts of total words.*"
1902 |    ]
1903 |   },
1904 |   {
1905 |    "cell_type": "code",
1906 |    "execution_count": 304,
1907 |    "metadata": {},
1908 |    "outputs": [
1909 |     {
1910 |      "data": {
1911 |       "text/plain": [
1912 |        "4.407147288400819"
1913 |       ]
1914 |      },
1915 |      "execution_count": 304,
1916 |      "metadata": {},
1917 |      "output_type": "execute_result"
1918 |     }
1919 |    ],
1920 |    "source": [
1921 |     "char_len = sum(len(w) for w in text1 if w.isalpha())\n",
1922 |     "total_words = len([w for w in text1 if w.isalpha() and w.lower() not in (\"d\", \"ll\", \"m\", \"re\", \"s\", \"t\", \"ve\")])\n",
1923 |     "\n",
1924 |     "char_len/total_words"
1925 |    ]
1926 |   },
1927 |   {
1928 |    "cell_type": "markdown",
1929 |    "metadata": {},
1930 |    "source": [
1931 |     "*The difference here isn't as nearly as great - partly because contractions aren't especially common in __Moby Dick__ - but if we care about the accuracy of our figures, we need to keep such things in mind.*"
1932 |    ]
1933 |   },
1934 |   {
1935 |    "cell_type": "markdown",
1936 |    "metadata": {},
1937 |    "source": [
1938 |     "###### 27. \n",
1939 |     "\n",
1940 |     "◑ Define a function called `vocab_size(text)` that has a single parameter for the text, and which returns the vocabulary size of the text.\n",
1941 |     "\n",
1942 |     "*Here is a basic version of that function. Also, the book hasn't yet covered docstrings for hand-crafted functions, but IMHO it's a good idea to get in the habit of writing descriptions of what your function does.*"
1943 |    ]
1944 |   },
1945 |   {
1946 |    "cell_type": "code",
1947 |    "execution_count": 302,
1948 |    "metadata": {},
1949 |    "outputs": [],
1950 |    "source": [
1951 |     "def vocab_size(text):\n",
1952 |     "    \"\"\"\n",
1953 |     "    Returns the number of words in a text.\n",
1954 |     "    \"\"\"\n",
1955 |     "    return len(text)"
1956 |    ]
1957 |   },
1958 |   {
1959 |    "cell_type": "code",
1960 |    "execution_count": 303,
1961 |    "metadata": {},
1962 |    "outputs": [
1963 |     {
1964 |      "data": {
1965 |       "text/plain": [
1966 |        "260819"
1967 |       ]
1968 |      },
1969 |      "execution_count": 303,
1970 |      "metadata": {},
1971 |      "output_type": "execute_result"
1972 |     }
1973 |    ],
1974 |    "source": [
1975 |     "vocab_size(text1)"
1976 |    ]
1977 |   },
1978 |   {
1979 |    "cell_type": "markdown",
1980 |    "metadata": {},
1981 |    "source": [
1982 |     "*But as outlined above, this includes punctuation, and enclitics.  This more complex version eliminates these:*"
1983 |    ]
1984 |   },
1985 |   {
1986 |    "cell_type": "code",
1987 |    "execution_count": 305,
1988 |    "metadata": {},
1989 |    "outputs": [],
1990 |    "source": [
1991 |     "def vocab_size(text):\n",
1992 |     "    \"\"\"\n",
1993 |     "    Returns the number of words in a text.  Only alphabetic strings\n",
1994 |     "    are considered.  Enclitics are removed from the count.\n",
1995 |     "    \"\"\"\n",
1996 |     "    \n",
1997 |     "    return len([w for w in text if w.isalpha() and w.lower not in (\"d\", \"ll\", \"m\", \"re\", \"s\", \"t\", \"ve\")])"
1998 |    ]
1999 |   },
2000 |   {
2001 |    "cell_type": "code",
2002 |    "execution_count": 306,
2003 |    "metadata": {},
2004 |    "outputs": [
2005 |     {
2006 |      "data": {
2007 |       "text/plain": [
2008 |        "218361"
2009 |       ]
2010 |      },
2011 |      "execution_count": 306,
2012 |      "metadata": {},
2013 |      "output_type": "execute_result"
2014 |     }
2015 |    ],
2016 |    "source": [
2017 |     "vocab_size(text1)"
2018 |    ]
2019 |   },
2020 |   {
2021 |    "cell_type": "markdown",
2022 |    "metadata": {},
2023 |    "source": [
2024 |     "###### 28. \n",
2025 |     "◑ Define a function `percent(word, text)` that calculates how often a given word occurs in a text, and expresses the result as a percentage.\n",
2026 |     "\n",
2027 |     "*We'll want to tabulate words regardless of their case, so I'm adding the method `.lower()` to the list comprehension.*"
2028 |    ]
2029 |   },
2030 |   {
2031 |    "cell_type": "code",
2032 |    "execution_count": 314,
2033 |    "metadata": {},
2034 |    "outputs": [],
2035 |    "source": [
2036 |     "def percent(word, text):\n",
2037 |     "    \"\"\"\n",
2038 |     "    Returns the percentage that a given word comprises in a text.\n",
2039 |     "    \"\"\"\n",
2040 |     "    word_count = len([w for w in text if w.lower() == word.lower()])\n",
2041 |     "    total_words = vocab_size(text)\n",
2042 |     "    return 100 * word_count/total_words "
2043 |    ]
2044 |   },
2045 |   {
2046 |    "cell_type": "code",
2047 |    "execution_count": 315,
2048 |    "metadata": {},
2049 |    "outputs": [
2050 |     {
2051 |      "data": {
2052 |       "text/plain": [
2053 |        "0.561455571278754"
2054 |       ]
2055 |      },
2056 |      "execution_count": 315,
2057 |      "metadata": {},
2058 |      "output_type": "execute_result"
2059 |     }
2060 |    ],
2061 |    "source": [
2062 |     "percent(\"whale\", text1)"
2063 |    ]
2064 |   },
2065 |   {
2066 |    "cell_type": "code",
2067 |    "execution_count": 319,
2068 |    "metadata": {},
2069 |    "outputs": [
2070 |     {
2071 |      "data": {
2072 |       "text/plain": [
2073 |        "0.6000779321989869"
2074 |       ]
2075 |      },
2076 |      "execution_count": 319,
2077 |      "metadata": {},
2078 |      "output_type": "execute_result"
2079 |     }
2080 |    ],
2081 |    "source": [
2082 |     "percent(\"God\", text3)"
2083 |    ]
2084 |   },
2085 |   {
2086 |    "cell_type": "markdown",
2087 |    "metadata": {},
2088 |    "source": [
2089 |     "###### 29. \n",
2090 |     "◑ We have been using sets to store vocabularies. Try the following Python expression: `set(sent3)` < `set(text1)`. Experiment with this using different arguments to `set()`. What does it do? Can you think of a practical application for this?"
2091 |    ]
2092 |   },
2093 |   {
2094 |    "cell_type": "code",
2095 |    "execution_count": 332,
2096 |    "metadata": {},
2097 |    "outputs": [
2098 |     {
2099 |      "data": {
2100 |       "text/plain": [
2101 |        "['In',\n",
2102 |        " 'the',\n",
2103 |        " 'beginning',\n",
2104 |        " 'God',\n",
2105 |        " 'created',\n",
2106 |        " 'the',\n",
2107 |        " 'heaven',\n",
2108 |        " 'and',\n",
2109 |        " 'the',\n",
2110 |        " 'earth',\n",
2111 |        " '.']"
2112 |       ]
2113 |      },
2114 |      "execution_count": 332,
2115 |      "metadata": {},
2116 |      "output_type": "execute_result"
2117 |     }
2118 |    ],
2119 |    "source": [
2120 |     "sent3"
2121 |    ]
2122 |   },
2123 |   {
2124 |    "cell_type": "code",
2125 |    "execution_count": 329,
2126 |    "metadata": {},
2127 |    "outputs": [
2128 |     {
2129 |      "data": {
2130 |       "text/plain": [
2131 |        "True"
2132 |       ]
2133 |      },
2134 |      "execution_count": 329,
2135 |      "metadata": {},
2136 |      "output_type": "execute_result"
2137 |     }
2138 |    ],
2139 |    "source": [
2140 |     "set(sent3) < set(text1)"
2141 |    ]
2142 |   },
2143 |   {
2144 |    "cell_type": "code",
2145 |    "execution_count": 330,
2146 |    "metadata": {},
2147 |    "outputs": [
2148 |     {
2149 |      "data": {
2150 |       "text/plain": [
2151 |        "True"
2152 |       ]
2153 |      },
2154 |      "execution_count": 330,
2155 |      "metadata": {},
2156 |      "output_type": "execute_result"
2157 |     }
2158 |    ],
2159 |    "source": [
2160 |     "set(sent3) < set(text3)"
2161 |    ]
2162 |   },
2163 |   {
2164 |    "cell_type": "code",
2165 |    "execution_count": 333,
2166 |    "metadata": {},
2167 |    "outputs": [
2168 |     {
2169 |      "data": {
2170 |       "text/plain": [
2171 |        "True"
2172 |       ]
2173 |      },
2174 |      "execution_count": 333,
2175 |      "metadata": {},
2176 |      "output_type": "execute_result"
2177 |     }
2178 |    ],
2179 |    "source": [
2180 |     "set(sent3) < set(text4)"
2181 |    ]
2182 |   },
2183 |   {
2184 |    "cell_type": "code",
2185 |    "execution_count": 323,
2186 |    "metadata": {},
2187 |    "outputs": [
2188 |     {
2189 |      "data": {
2190 |       "text/plain": [
2191 |        "False"
2192 |       ]
2193 |      },
2194 |      "execution_count": 323,
2195 |      "metadata": {},
2196 |      "output_type": "execute_result"
2197 |     }
2198 |    ],
2199 |    "source": [
2200 |     "set(sent3) < set(text5)"
2201 |    ]
2202 |   },
2203 |   {
2204 |    "cell_type": "markdown",
2205 |    "metadata": {},
2206 |    "source": [
2207 |     "*The expression evaluates whether the set on the left is a subset of the set of the right.  It could be used to determine if one set was drawn from another, i.e., to try to locate which text a sentence was taken from.  However, this would generate a large number of false positives in the sentence was sufficiently short, as can be seen above.  It could also be used to compare the similarities between two texts - with the caveat that one text would have to be significantly shorter than the other.*"
2208 |    ]
2209 |   }
2210 |  ],
2211 |  "metadata": {
2212 |   "kernelspec": {
2213 |    "display_name": "Python 3",
2214 |    "language": "python",
2215 |    "name": "python3"
2216 |   },
2217 |   "language_info": {
2218 |    "codemirror_mode": {
2219 |     "name": "ipython",
2220 |     "version": 3
2221 |    },
2222 |    "file_extension": ".py",
2223 |    "mimetype": "text/x-python",
2224 |    "name": "python",
2225 |    "nbconvert_exporter": "python",
2226 |    "pygments_lexer": "ipython3",
2227 |    "version": "3.7.3"
2228 |   }
2229 |  },
2230 |  "nbformat": 4,
2231 |  "nbformat_minor": 2
2232 | }
2233 | 


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/Chapter 03/Nationalities.csv:
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https://raw.githubusercontent.com/STRZGR/Natural-Language-Processing-with-Python-Analyzing-Text-with-the-Natural-Language-Toolkit/07835b68d1f7e7d274baf24f409e5345a73f3110/Chapter 03/Nationalities.csv


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/Chapter 03/tranzlator.yml:
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   1 | a: ""
   2 | abbatial: abbashul
   3 | abhorred: abhorrd
   4 | about: bout
   5 | absorbed: absorbd
   6 | abstinential: abstinenshul
   7 | accelerometer: accelerometr
   8 | accepts: acceptz
   9 | access: acces
  10 | accessibility: accesibility
  11 | accessories: accessoriez
  12 | accidential: accidenshul
  13 | accompanying: accompanyin
  14 | accomplished: accomplishd
  15 | according: accordin
  16 | account: akownt
  17 | accounting: accouting
  18 | accrementitial: accrementishul
  19 | accumulating: accumulatin
  20 | accusation: accusashun
  21 | accustomed: accustomd
  22 | achilles: achillez
  23 | acquired: acquird
  24 | acquiring: acquirin
  25 | acquisition: acquizishun
  26 | across: acros
  27 | action: akshun
  28 | actions: akshuns
  29 | actual: akshul
  30 | actually: akshully
  31 | added: addd
  32 | adding: addin
  33 | addition: addishun
  34 | additions: addishuns
  35 | address: addres
  36 | addresses: addressez
  37 | adds: addz
  38 | administration: administrashun
  39 | admission: admishun
  40 | admitted: admittd
  41 | adopters: adopterz
  42 | ads: adz
  43 | advanced: advancd
  44 | advantages: advantagez
  45 | advential: advenshul
  46 | advertisers: advertiserz
  47 | advertising: advertisin
  48 | advised: advisd
  49 | advisors: advisorz
  50 | affiliation: affiliashun
  51 | aforelinked: aforelinkd
  52 | aforementioned: aforemenshuned
  53 | after: aftr
  54 | agential: agenshul
  55 | ages: agez
  56 | agreed: agred
  57 | agrees: agreez
  58 | agrestial: agresshul
  59 | aims: aimz
  60 | airs: airz
  61 | airtunes: airtunez
  62 | allowed: allowd
  63 | allowing: allowin
  64 | allows: allowz
  65 | alpha: alfa
  66 | alternating: alternatin
  67 | alternative: alternativ
  68 | alternatives: alternativez
  69 | always: alwayz
  70 | amazing: amazin
  71 | analyzing: analyzin
  72 | anchored: anchord
  73 | and: an
  74 | animated: animatd
  75 | animation: animashun
  76 | animations: animashuns
  77 | annoucing: annoucin
  78 | announced: announcd
  79 | announcing: announcin
  80 | annoyed: annoyd
  81 | annoying: annoyin
  82 | another: anothr
  83 | answer: anzwr
  84 | answered: anzwerd
  85 | answering: answerin
  86 | answers: anzwrs
  87 | anteinitial: anteinishul
  88 | antenatalitial: antenatalishul
  89 | antialiasing: antialiasin
  90 | anticipated: anticipatd
  91 | anticipation: anticipashun
  92 | antipestilential: antipestilenshul
  93 | antiprudential: antiprudenshul
  94 | anything: anythin
  95 | aperature: aperschur
  96 | apologies: apologiez
  97 | apostrophe: apostrofe
  98 | appalling: appallin
  99 | appealed: appeald
 100 | appeared: appeard
 101 | appears: appearz
 102 | application: applicashun
 103 | applications: applicashuns
 104 | applies: appliez
 105 | approved: approvd
 106 | approving: approvin
 107 | apps: appz
 108 | aquantive: aquantiv
 109 | architecture: architecchur
 110 | archive: archiv
 111 | are: r
 112 | argued: argud
 113 | arising: arisin
 114 | arrived: arrivd
 115 | articles: articlez
 116 | asisted: assistd
 117 | asked: askd
 118 | asking: askin
 119 | asks: askz
 120 | assembling: assemblin
 121 | assets: assetz
 122 | assimilation: assimilashun
 123 | associated: associatd
 124 | assumed: assumd
 125 | assuming: assumin
 126 | assumption: assumpshun
 127 | astonishing: astonishin
 128 | astounding: astoundin
 129 | atrophy: atrofy
 130 | attached: attachd
 131 | attacker: attackr
 132 | attacks: attackz
 133 | attempted: attemptd
 134 | attempting: attemptin
 135 | attendees: attendeez
 136 | attention: attenshun
 137 | attitudes: attitudez
 138 | authentication: authenticashun
 139 | authored: authord
 140 | authorities: authoritiez
 141 | authorized: authorizd
 142 | authors: authorz
 143 | autosaving: autosavin
 144 | avoiding: avoidin
 145 | awesome: awsum
 146 | back: bak
 147 | backed: backd
 148 | background: bakground
 149 | backing: backin
 150 | backup: bakup
 151 | backups: backupz
 152 | baffled: baffld
 153 | baked: bakd
 154 | balanopreputial: balanoprepushul
 155 | balked: balkd
 156 | balkled: balkd
 157 | ballmer: ballmr
 158 | bandwidth: bandwif
 159 | banker: bankr
 160 | banking: bankin
 161 | banning: bannin
 162 | based: basd
 163 | bastards: bastardz
 164 | be: bees
 165 | beaing: bean
 166 | beaking: breakin
 167 | because: cuz
 168 | becomes: becomez
 169 | before: "b4"
 170 | beginning: beginnin
 171 | beijing: beijin
 172 | being: bein
 173 | beleaguered: beleaguerd
 174 | believe: bleev
 175 | believes: believez
 176 | believing: believin
 177 | belongs: belongz
 178 | benchmarks: benchmarkz
 179 | bends: bendz
 180 | beneficential: beneficenshul
 181 | benefits: benefitz
 182 | bestial: besshul
 183 | betrays: betrayz
 184 | better: bettr
 185 | between: tween
 186 | bidential: bidenshul
 187 | big: hooj
 188 | bigential: bigenshul
 189 | bigger: biggr
 190 | billboards: billboardz
 191 | billed: billd
 192 | binaries: binariez
 193 | birthday: birfdai
 194 | bitangential: bitangenshul
 195 | blocks: blockz
 196 | bloggers: bloggerz
 197 | blogging: bloggin
 198 | blows: blowz
 199 | board: bord
 200 | boing: boin
 201 | "book's": bookz
 202 | booked: bookd
 203 | bookies: bookiez
 204 | bookmarks: bookmarkz
 205 | books: bookz
 206 | bootlegged: bootleggd
 207 | bootlegging: bootleggin
 208 | borders: borderz
 209 | boring: borin
 210 | boss: bos
 211 | bought: buyd
 212 | bow: bowage
 213 | boy: boi
 214 | branching: branchin
 215 | branded: brandd
 216 | branding: brandin
 217 | breaks: breakz
 218 | breed: bred
 219 | brewed: brewd
 220 | bricks: brickz
 221 | briefed: briefd
 222 | brightness: brightnes
 223 | bring: brin
 224 | bringing: bringin
 225 | brings: bringz
 226 | broken: brokd
 227 | browser: browsr
 228 | browsers: browserz
 229 | browsing: browsin
 230 | building: buildin
 231 | burned: burnd
 232 | burrows: burrowz
 233 | business: busines
 234 | businessweek: businesweek
 235 | busted: bustd
 236 | buying: buyin
 237 | buys: buyz
 238 | bye: kthxbye
 239 | cables: cablez
 240 | cadential: cadenshul
 241 | calculated: calculatd
 242 | called: calld
 243 | calling: callin
 244 | calls: callz
 245 | candidates: candidatez
 246 | canned: cannd
 247 | cans: canz
 248 | capillitial: capillishul
 249 | capitalization: capitalizashun
 250 | capping: cappin
 251 | caps: capz
 252 | capture: capchure
 253 | captured: capturd
 254 | cardinalitial: cardinalishul
 255 | carrier: carrir
 256 | cars: carz
 257 | cat: kitteh
 258 | caucuses: caucusez
 259 | caution: caushun
 260 | celebrating: celebratin
 261 | celestial: celesshul
 262 | cellphone: cellfone
 263 | center: centr
 264 | cents: sents
 265 | challenges: challengez
 266 | champions: championz
 267 | changed: changd
 268 | changes: changez
 269 | changing: changin
 270 | channels: channelz
 271 | characters: characterz
 272 | charged: chargd
 273 | charges: chargez
 274 | charging: chargin
 275 | chatter: chattr
 276 | cheaper: cheapr
 277 | checking: checkin
 278 | cheer: cher
 279 | cheeseburger: cheezburger
 280 | cheeseburgers: cheezburgers
 281 | chipped: chippd
 282 | choose: chooz
 283 | choosing: choosin
 284 | christopher: christophr
 285 | circumferential: circumferenshul
 286 | circumspatial: circumspashul
 287 | circumstantial: circumstanshul
 288 | cited: citd
 289 | cities: citiez
 290 | claims: claimz
 291 | class: clas
 292 | clearer: clearr
 293 | clever: clevr
 294 | clicking: clickin
 295 | clients: clientz
 296 | closes: closez
 297 | closing: closin
 298 | coders: coderz
 299 | coding: codin
 300 | coeffluential: coeffluenshul
 301 | coessential: coessenshul
 302 | coincides: coincidez
 303 | coinitial: coinishul
 304 | collaboration: collaborashun
 305 | colleagues: colleaguez
 306 | collection: collecshun
 307 | collections: collecshuns
 308 | collects: collectz
 309 | columns: columnz
 310 | combination: combinashun
 311 | comes: comez
 312 | coming: comin
 313 | comitial: comishul
 314 | commands: commandz
 315 | comments: commentz
 316 | commercial: commershul
 317 | commercials: commershuls
 318 | commission: commishun
 319 | communicated: speekd
 320 | communicating: communicatin
 321 | commuter: commutr
 322 | companies: companiez
 323 | compares: comparez
 324 | comparing: comparin
 325 | compelling: compellin
 326 | compensation: compensashun
 327 | competing: competin
 328 | competition: competishun
 329 | competitors: competitorz
 330 | compilation: compilashun
 331 | compiled: compild
 332 | complacential: complacenshul
 333 | complained: complaind
 334 | complaining: complainin
 335 | complains: complainz
 336 | complaints: complaintz
 337 | completed: completd
 338 | complicated: complicatd
 339 | components: componentz
 340 | comprised: comprisd
 341 | compromise: compromize
 342 | compromised: compromisd
 343 | computer: computr
 344 | computers: computerz
 345 | computing: computin
 346 | concerns: concernz
 347 | concluded: concludd
 348 | concludes: concludez
 349 | conclusion: conclushun
 350 | concordantial: concordanshul
 351 | condenses: condensez
 352 | condition: condishun
 353 | conditions: condishuns
 354 | conferential: conferenshul
 355 | confessing: confessin
 356 | confidential: confidenshul
 357 | configuration: configurashun
 358 | confines: confinez
 359 | confirmation: confirmashun
 360 | confirmed: confirmd
 361 | confronted: confrontd
 362 | confusion: confushun
 363 | congratulations: congratulashuns
 364 | congress: congres
 365 | congruential: congruenshul
 366 | connected: connectd
 367 | connection: connecshun
 368 | connections: connecshuns
 369 | consciousness: consciousnes
 370 | consequential: consequenshul
 371 | consider: considr
 372 | considerations: considerashuns
 373 | considered: considerd
 374 | considering: considerin
 375 | considers: considerz
 376 | considred: considerd
 377 | consiousness: sonsciousnes
 378 | consists: consistz
 379 | consortial: consorshul
 380 | constrained: constraind
 381 | consubstantial: consubstanshul
 382 | consulting: consultin
 383 | consumer: consumr
 384 | consumers: consumerz
 385 | consumes: consumez
 386 | consuming: consumin
 387 | consummated: consummatd
 388 | contacted: contactd
 389 | contains: containz
 390 | contangential: contangenshul
 391 | contents: contentz
 392 | contingential: contingenshul
 393 | continued: continud
 394 | continues: continuez
 395 | contradiction: contradicshun
 396 | contrived: contrivd
 397 | controlled: controlld
 398 | controller: controllr
 399 | controls: controlz
 400 | controversial: controvershul
 401 | convention: convenshun
 402 | conventional: convenshunal
 403 | conversation: conversashun
 404 | conversions: convershuns
 405 | converted: convertd
 406 | convinced: convincd
 407 | coordinates: coordinatez
 408 | coordination: coordinashun
 409 | copied: copid
 410 | copies: copiez
 411 | corporation: corporashun
 412 | correspondential: correspondenshul
 413 | costs: costz
 414 | cotangential: cotangenshul
 415 | could: cud
 416 | covering: coverin
 417 | covers: coverz
 418 | cracking: crackin
 419 | crapped: crappd
 420 | creaks: creakz
 421 | created: creatd
 422 | creating: creatin
 423 | creation: creashun
 424 | creations: creashuns
 425 | credential: credenshul
 426 | credentials: credenshuls
 427 | crendital: credenshul
 428 | criticism: critisizm
 429 | critics: criticz
 430 | crossed: crossd
 431 | crowes: crowez
 432 | crowned: crownd
 433 | crumbling: crumblin
 434 | cubes: cubez
 435 | curatial: curashul
 436 | customer: customr
 437 | customers: customerz
 438 | cutter: cuttr
 439 | cutting: cuttin
 440 | damages: damagez
 441 | danger: dangr
 442 | daring: darin
 443 | day: dai
 444 | days: dais
 445 | dead: ded
 446 | deals: dealz
 447 | debuted: debutd
 448 | december: decembr
 449 | deception: decepshun
 450 | decided: decidd
 451 | decides: decidez
 452 | deciding: decidin
 453 | decision: decishun
 454 | decisions: decishuns
 455 | decrypting: decryptin
 456 | dedicated: dedicatd
 457 | deduction: deducshun
 458 | deductions: deducshuns
 459 | defaults: defaultz
 460 | defeats: defeatz
 461 | defective: defectiv
 462 | deferential: deferenshul
 463 | deficiencies: deficienciez
 464 | defining: definin
 465 | definition: definishun
 466 | definitions: definishuns
 467 | delays: delayz
 468 | delicious: delishus
 469 | deliver: delivr
 470 | delivered: deliverd
 471 | delivering: deliverin
 472 | delivers: deliverz
 473 | delphi: delfi
 474 | demanding: demandin
 475 | demographics: demographicz
 476 | demos: demoz
 477 | denver: denvr
 478 | denying: denyin
 479 | dependencies: dependenciez
 480 | deposition: deposishun
 481 | depreciation: depreciashun
 482 | depressing: depresin
 483 | described: describd
 484 | describes: describez
 485 | description: descripshun
 486 | descriptions: descripshuns
 487 | deserved: deservd
 488 | deserves: deservez
 489 | designed: designd
 490 | designer: designr
 491 | designers: designerz
 492 | designing: designin
 493 | designs: designz
 494 | desperation: desperashun
 495 | destined: destind
 496 | destroyed: destroyd
 497 | detailed: detaild
 498 | details: detailz
 499 | determination: determinashun
 500 | determined: determind
 501 | developed: developd
 502 | developer: developr
 503 | developers: developrz
 504 | developing: developin
 505 | devices: devicez
 506 | "didn't": didnt
 507 | died: did
 508 | differences: differencez
 509 | differencing: differencin
 510 | differential: differenshul
 511 | digging: diggin
 512 | digitizing: digitizin
 513 | diluted: dilutd
 514 | dimension: dimenshun
 515 | dinner: dinnr
 516 | dipped: dippd
 517 | direction: direcshun
 518 | directions: direcshuns
 519 | directories: directoriez
 520 | directors: directorz
 521 | disabled: diabld
 522 | disagreeing: disagreein
 523 | disappearing: disapperin
 524 | disappointed: disappointd
 525 | disappointing: disappointin
 526 | discounted: discountd
 527 | discovered: discoverd
 528 | discussion: discushun
 529 | discussions: discushuns
 530 | disliked: dislikd
 531 | dismiss: dismis
 532 | dismissed: dismissd
 533 | dismisses: dismisez
 534 | dismissing: dismissin
 535 | disorder: disordr
 536 | dispatches: dispatchez
 537 | displayed: displayd
 538 | displays: displayz
 539 | disputed: disputd
 540 | distinction: distincshun
 541 | distributed: distributd
 542 | distribution: distribushun
 543 | ditched: ditchd
 544 | division: divishun
 545 | divisions: divishuns
 546 | do: doez
 547 | docs: docz
 548 | doctored: doctord
 549 | documentation: documentashun
 550 | dodging: dodgin
 551 | does: doez
 552 | dogged: doggd
 553 | doing: doin
 554 | dollars: dollarz
 555 | "don't": dun
 556 | donation: donashun
 557 | donations: donashuns
 558 | done: dun
 559 | doomed: doomd
 560 | doors: doorz
 561 | doubled: doubld
 562 | doubters: doubterz
 563 | doubting: doubtin
 564 | downgrading: downgradin
 565 | downloading: downloadin
 566 | downloads: downloadz
 567 | dressed: dressd
 568 | drive: driv
 569 | drivers: driverz
 570 | drives: drivez
 571 | driving: drivin
 572 | dropped: droppd
 573 | drops: dropz
 574 | drowned: drownd
 575 | during: durin
 576 | dvds: dvdz
 577 | eagles: eaglez
 578 | earlier: earlir
 579 | easier: easir
 580 | eclipsing: eclipsin
 581 | economics: economicz
 582 | edges: edgez
 583 | editing: editin
 584 | edition: edishun
 585 | editors: editorz
 586 | edits: editz
 587 | education: edumacashun
 588 | edwards: edwardz
 589 | effects: effectz
 590 | either: eithr
 591 | election: elecshun
 592 | electropotential: electropotenshul
 593 | elements: elementz
 594 | elmer: elmr
 595 | elongated: elongatd
 596 | eloquential: eloquenshul
 597 | emailed: emaild
 598 | emerging: emergin
 599 | emphasis: emfasiz
 600 | emphasizing: emphasizin
 601 | employed: employd
 602 | employees: employeez
 603 | emulation: emulashun
 604 | emulations: emulashuns
 605 | enabled: enabld
 606 | enables: enablez
 607 | enabling: enablin
 608 | encountering: encounterin
 609 | encourages: encouragez
 610 | encouraging: encouragin
 611 | encryption: encrypshun
 612 | endeavors: endeavorz
 613 | ended: endd
 614 | ending: endin
 615 | endless: endles
 616 | endorsed: endorsd
 617 | endorses: endorsez
 618 | endorsing: endorsin
 619 | ends: endz
 620 | enemies: enemiez
 621 | engineering: engineerin
 622 | engineers: engineerz
 623 | enhanced: enhancd
 624 | enhances: enhanez
 625 | enjoyed: enjoyd
 626 | enjoying: enjoyin
 627 | enlightened: enlightend
 628 | enter: entr
 629 | entrenched: entrenchd
 630 | entries: entriez
 631 | enumeration: enumerashun
 632 | epididymodeferential: epididymodeferenshul
 633 | equation: equashun
 634 | equidistantial: equidistanshul
 635 | equinoctial: equinocshul
 636 | equipotential: equipotenshul
 637 | equipped: equippd
 638 | equispatial: equispashul
 639 | equitangential: equitangenshul
 640 | errors: errorz
 641 | escaping: escapin
 642 | essential: essenshul
 643 | established: establishd
 644 | establishing: establishin
 645 | ether: ethr
 646 | eucharistial: eucharisshul
 647 | euphemisms: eufemisms
 648 | evaluating: evaluatin
 649 | events: eventz
 650 | eventually: evenshully
 651 | ever: evr
 652 | every: evry
 653 | everyone: evryone
 654 | everything: everythin
 655 | evidential: evidenshul
 656 | evokes: evokez
 657 | evolution: evolushun
 658 | evolving: evolvin
 659 | exactly: egsaktly
 660 | examination: examinashun
 661 | examples: examplez
 662 | exasperated: exasperatd
 663 | excellent: awsum
 664 | exception: excepshun
 665 | excited: excitd
 666 | exciting: excitin
 667 | excrementitial: excrementishul
 668 | excrescential: excrescenshul
 669 | executing: executin
 670 | executive: executiv
 671 | executives: executivez
 672 | exhaustive: exhaustiv
 673 | existed: existd
 674 | existential: existenshul
 675 | existing: existin
 676 | exists: existz
 677 | expanded: expandd
 678 | expanding: expandin
 679 | expected: expectd
 680 | expediential: expedienshul
 681 | expenditures: expenditurez
 682 | experienced: experiencd
 683 | experiences: experiencez
 684 | experiential: experienshul
 685 | explained: explaind
 686 | explains: explainz
 687 | explanation: explanashun
 688 | explanations: explanashuns
 689 | exponential: exponenshul
 690 | exported: exportd
 691 | express: expres
 692 | expression: expreshun
 693 | expressions: expreshuns
 694 | extends: extendz
 695 | extensions: extenshuns
 696 | extensive: extensiv
 697 | extraessential: extraessenshul
 698 | fabrication: fabricashun
 699 | faces: facez
 700 | factitial: factishul
 701 | factories: factoriez
 702 | factors: factorz
 703 | failed: faild
 704 | failing: failin
 705 | failures: failurez
 706 | fake: faek
 707 | fans: fanz
 708 | fascinating: fascintatin
 709 | faster: fastr
 710 | fastscripts: fastscriptz
 711 | feathers: featherz
 712 | feature: feachur
 713 | featured: featurd
 714 | features: featurez
 715 | featuring: featurin
 716 | feed: fed
 717 | feel: feelz
 718 | feeling: feelin
 719 | feels: feelz
 720 | fees: feez
 721 | fetial: feshul
 722 | fever: fevr
 723 | fewer: fewr
 724 | fiddling: fiddlin
 725 | figures: figurez
 726 | figuring: figurin
 727 | filed: fild
 728 | files: filez
 729 | filter: filtr
 730 | financial: finanshul
 731 | finder: findr
 732 | finding: findin
 733 | fingers: fingerz
 734 | finished: finishd
 735 | fired: fird
 736 | first: furst
 737 | fitness: fitnes
 738 | fits: fitz
 739 | fiver: fivr
 740 | fixed: fixd
 741 | fixes: fixez
 742 | fixing: fixin
 743 | flavored: flavord
 744 | flawed: flawd
 745 | floating: floatin
 746 | focused: focusd
 747 | folder: foldr
 748 | folks: folkz
 749 | following: followin
 750 | fonts: fontz
 751 | football: futball
 752 | for: "4"
 753 | forced: forcd
 754 | forcing: forcin
 755 | foregoing: foregoin
 756 | forestial: foresshul
 757 | formats: formatz
 758 | formatted: formattd
 759 | formed: formd
 760 | former: formr
 761 | forms: formz
 762 | fortunes: fortunez
 763 | forums: forumz
 764 | found: findz
 765 | foundation: foundashun
 766 | four: "4"
 767 | foxytunes: foxytunez
 768 | frames: framez
 769 | fraser: frasr
 770 | friday: fridai
 771 | fried: frid
 772 | friend: frend
 773 | friends: frenz
 774 | from: frum
 775 | frommer: frommr
 776 | front: frunt
 777 | frontier: frontir
 778 | frustrated: frustratd
 779 | frying: fryin
 780 | fuck: "f&^%"
 781 | fucking: "f&^%ing"
 782 | fueled: fueld
 783 | full: fol
 784 | function: funcshun
 785 | functions: funcshuns
 786 | furnished: furnishd
 787 | further: furthr
 788 | galleries: galleriez
 789 | games: gamez
 790 | gaping: gapin
 791 | gartner: gartnr
 792 | gatekeeper: gatekeepr
 793 | gave: gaev
 794 | gawker: gawkr
 795 | generated: generatd
 796 | generates: generatez
 797 | generating: generatin
 798 | generation: generashun
 799 | gentilitial: gentilishul
 800 | gestures: gesturez
 801 | get: git
 802 | gets: getz
 803 | getting: gettin
 804 | giant: hooj
 805 | giants: giantz
 806 | ginned: ginnd
 807 | girl: gurl
 808 | give: giv
 809 | gives: givez
 810 | giving: givin
 811 | gloating: gloatin
 812 | glorified: glorifid
 813 | goes: goez
 814 | going: goin
 815 | good: gud
 816 | goodies: goodiez
 817 | graggers: graggerz
 818 | graphed: grafd
 819 | graphic: grafic
 820 | graphics: grafic
 821 | gratification: gratificashun
 822 | greater: greatr
 823 | gripped: grippd
 824 | groans: groanz
 825 | grounds: groundz
 826 | growing: growin
 827 | grows: growz
 828 | gruber: groober
 829 | guaranteed: guaranted
 830 | guess: gues
 831 | guessed: guessd
 832 | guessing: guessin
 833 | guided: guidd
 834 | guts: gutz
 835 | guys: guyz
 836 | hacking: hackin
 837 | half: haf
 838 | handed: handd
 839 | handicaps: handicapz
 840 | handles: handlez
 841 | hands: hanz
 842 | hanging: hangin
 843 | happend: happend
 844 | happened: happend
 845 | happening: happenin
 846 | happens: happenz
 847 | happy: happeh
 848 | harder: hardr
 849 | have: has
 850 | having: havin
 851 | head: hed
 852 | headed: headd
 853 | heading: headin
 854 | heads: headz
 855 | heard: herd
 856 | hearing: hearin
 857 | hearings: hearingz
 858 | heartwarming: heartwarmin
 859 | hearwarming: heartwarmin
 860 | heels: heelz
 861 | hello: "y halo thar"
 862 | help: halp
 863 | helped: helpd
 864 | helping: helpin
 865 | helps: helpz
 866 | here: her
 867 | "here's": heers
 868 | heroes: heroez
 869 | hi: "oh hai"
 870 | higher: highr
 871 | highlight: highlite
 872 | highlighting: highlightin
 873 | highlights: highlightz
 874 | himself: him
 875 | hints: hintz
 876 | hired: hird
 877 | his: hims
 878 | hits: hitz
 879 | holder: holdr
 880 | holders: holderz
 881 | holding: holdin
 882 | holdings: holdingz
 883 | holds: holdz
 884 | holiday: holidai
 885 | holidays: holidais
 886 | hoped: hopd
 887 | hopelessly: hopelesly
 888 | hoping: hopin
 889 | hosting: hostin
 890 | hours: hourz
 891 | house: houz
 892 | hovering: hoverin
 893 | however: howevr
 894 | hughes: hughez
 895 | humorless: humorles
 896 | hundred: hundrd
 897 | hundreds: hundredz
 898 | hunger: hungr
 899 | hurdles: hurdlez
 900 | hurts: hurtz
 901 | hyperreverential: hyperreverenshul
 902 | hyperspatial: hyperspashul
 903 | "i'm": me
 904 | "i've": "i has"
 905 | icons: iconz
 906 | ideas: ideaz
 907 | identified: identifid
 908 | idiots: idiotz
 909 | illusion: illushun
 910 | illustration: illustrashun
 911 | imacs: imacz
 912 | images: imagez
 913 | imaging: imagin
 914 | imaginig: imaginin
 915 | imagining: imaginin
 916 | impartial: imparshul
 917 | implementation: implementashun
 918 | implemented: implementd
 919 | implied: implid
 920 | implies: impliez
 921 | impressed: impresd
 922 | impressions: impreshuns
 923 | improve: improov
 924 | improved: improovd
 925 | improvements: improovements
 926 | imprudential: imprudenshul
 927 | inception: incepshun
 928 | inches: inchez
 929 | included: includd
 930 | includes: includez
 931 | including: includin
 932 | inconsequential: inconsequenshul
 933 | increased: increasd
 934 | increases: increasez
 935 | increasing: increasin
 936 | indeed: inded
 937 | indentation: indentashun
 938 | indentations: indentashuns
 939 | indicated: indicatd
 940 | indicates: indicatez
 941 | indifferential: indifferenshul
 942 | individuals: individualz
 943 | indulgential: indulgenshul
 944 | inequipotential: inequipotenshul
 945 | inertial: inershul
 946 | inessential: inessenshul
 947 | infection: infecshun
 948 | infects: infectz
 949 | inferential: inferenshul
 950 | influenced: influencd
 951 | influential: influenshul
 952 | infographic: infografic
 953 | information: informashun
 954 | informed: informd
 955 | infrabestial: infrabesshul
 956 | infracelestial: infracelesshul
 957 | infuriating: infriatin
 958 | initial: inishul
 959 | injection: injecshun
 960 | innocent: innosent
 961 | inquirer: inquirr
 962 | inspection: inspecshun
 963 | inspiration: inspirashun
 964 | inspired: inspird
 965 | inspiring: inspirin
 966 | installation: installashun
 967 | installations: installashuns
 968 | installed: installd
 969 | installer: installr
 970 | installing: installin
 971 | instantial: instanshul
 972 | instruction: instrucshun
 973 | instructions: instrucshuns
 974 | insubstantial: insubstanshul
 975 | integrated: integratd
 976 | integrates: integratez
 977 | integration: integrashn
 978 | integrodifferential: integrodifferenshul
 979 | intelligential: intelligenshul
 980 | intended: intendd
 981 | intentions: intenshuns
 982 | interaction: interacshun
 983 | interactive: interactiv
 984 | interequinoctial: interequinocshul
 985 | interested: interestd
 986 | interesting: interestin
 987 | interfaces: interfacez
 988 | interfacing: interfacin
 989 | interferential: interferenshul
 990 | intergential: intergenshul
 991 | internet: internets
 992 | internuptial: internupshul
 993 | interpolation: interpolashun
 994 | interpolations: interpolashuns
 995 | interspatial: interspashul
 996 | interstitial: interstishul
 997 | interviews: interviewz
 998 | introduced: introducd
 999 | introduces: introducez
1000 | introducing: introducin
1001 | introduction: introducshun
1002 | inverts: invertz
1003 | invested: investd
1004 | investigating: investigatin
1005 | investigative: investigativ
1006 | investors: investorz
1007 | invokes: invokez
1008 | involved: involvd
1009 | iphone: ifone
1010 | iphonedrive: ifonedrive
1011 | iphones: ifonez
1012 | iphoto: ifoto
1013 | ipods: ipodz
1014 | irredential: irredenshul
1015 | irreverential: irreverenshul
1016 | is: iz
1017 | "isn't": isnt
1018 | isolated: isolatd
1019 | issues: issuez
1020 | "it's": iz
1021 | items: itemz
1022 | iterating: iteratin
1023 | its: itz
1024 | itunes: itunez
1025 | jackass: jackas
1026 | jackassyness: jackasynes
1027 | james: jamez
1028 | jaws: jawz
1029 | jesus: jebus
1030 | jibes: jibez
1031 | jiminy: omgwtfbbq
1032 | jobs: jobz
1033 | joined: joind
1034 | joining: joinin
1035 | joking: jokin
1036 | jones: jonez
1037 | jostling: jostlin
1038 | journalists: journalistz
1039 | judged: judgd
1040 | judging: judgin
1041 | jumped: jumpd
1042 | jumping: jumpin
1043 | jurisprudential: jurisprudenshul
1044 | just: jus
1045 | keeping: keepin
1046 | keeps: keepz
1047 | keyboards: keyboardz
1048 | keys: keyz
1049 | keywords: keywordz
1050 | kicking: kickin
1051 | kidding: kiddin
1052 | kids: kidz
1053 | killed: killd
1054 | killer: killr
1055 | know: knoe
1056 | knows: knowz
1057 | labeled: labeld
1058 | labeling: labelin
1059 | labelled: labeld
1060 | labels: labelz
1061 | laboratories: laboratoriez
1062 | labs: labz
1063 | ladies: ladiez
1064 | landed: landd
1065 | laptops: laptopz
1066 | larger: largr
1067 | laser: lazr
1068 | later: latr
1069 | latter: lattr
1070 | laugh: laff
1071 | launched: launchd
1072 | laws: lawz
1073 | layers: layerz
1074 | layouts: layoutz
1075 | lazytwitter: lazytwittr
1076 | leaders: leaderz
1077 | leading: leadin
1078 | leaks: leakz
1079 | learned: learnd
1080 | learning: lernin
1081 | leave: leef
1082 | legislature: legislachur
1083 | legs: legz
1084 | lens: lenz
1085 | leopard: lepprd
1086 | less: les
1087 | lets: letz
1088 | letter: lettr
1089 | letters: letterz
1090 | levels: levelz
1091 | leveraging: leveragin
1092 | levereaging: leveragin
1093 | licensed: licensd
1094 | licensing: licensin
1095 | like: liek
1096 | liked: likd
1097 | limitation: limitashun
1098 | limitations: limitashuns
1099 | limited: limitd
1100 | limits: limitz
1101 | lined: lind
1102 | lines: linez
1103 | lingering: lingerin
1104 | linkblogging: linkbloggin
1105 | links: linkz
1106 | listened: listend
1107 | lists: listz
1108 | little: lil
1109 | live: liv
1110 | living: livin
1111 | loaded: loadd
1112 | loading: loadin
1113 | localised: localisd
1114 | location: locashun
1115 | logicians: logicianz
1116 | logs: logz
1117 | longer: longr
1118 | looked: lookd
1119 | looking: lukingz
1120 | looks: lookz
1121 | losing: losin
1122 | lots: lawts
1123 | love: luv
1124 | loved: lovd
1125 | lovely: luvly
1126 | loves: luvz
1127 | lucrative: lucrativ
1128 | lunistitial: lunistishul
1129 | lying: lyin
1130 | macbooks: macbookz
1131 | machine: masheen
1132 | machines: masheens
1133 | macs: macz
1134 | magazines: magazinez
1135 | mailboxes: mailboxez
1136 | maintained: maintaind
1137 | maintaining: maintainin
1138 | make: mak
1139 | maker: makr
1140 | makers: makerz
1141 | makes: makez
1142 | making: makin
1143 | malicious: malishus
1144 | manager: managr
1145 | managers: managerz
1146 | manages: managez
1147 | managing: managin
1148 | manipulation: manipulashun
1149 | manning: manning
1150 | manufacture: manufacchur
1151 | manufacturing: manufacturin
1152 | many: "lotz da"
1153 | mapping: mappin
1154 | maps: mapz
1155 | market: markit
1156 | marketing: marketin
1157 | markets: marketz
1158 | marshals: marshalz
1159 | martial: marshul
1160 | masks: maskz
1161 | mass: mas
1162 | matter: mattr
1163 | mattered: matterd
1164 | maximizing: maximizin
1165 | may: cud
1166 | meaning: meanin
1167 | means: meanz
1168 | measures: measurez
1169 | measuring: measurin
1170 | mechanisms: mechanismz
1171 | meeting: mettin
1172 | megaphone: megafone
1173 | member: membr
1174 | members: memberz
1175 | memoirs: memoirz
1176 | memory: mems
1177 | mention: menshun
1178 | mentioned: menshund
1179 | merchandise: merchandize
1180 | mess: mes
1181 | message: mesage
1182 | messages: mesagez
1183 | messenger: messengr
1184 | migration: migrashun
1185 | minded: mindd
1186 | minds: mindz
1187 | miniaturization: miniaturizashun
1188 | minutes: minutez
1189 | minutial: minushul
1190 | mischaracterization: mischaracterizashun
1191 | misd: missd
1192 | misleading: misleadin
1193 | miss: mis
1194 | missed: misd
1195 | misses: misez
1196 | missing: missin
1197 | mission: mishun
1198 | missions: mishuns
1199 | mistakes: mistakez
1200 | misunderstanding: misunderstandin
1201 | mocking: mockn
1202 | models: modelz
1203 | monday: mondai
1204 | months: monthz
1205 | more: moar
1206 | morning: mornin
1207 | mother: mothr
1208 | motion: moshun
1209 | move: moov
1210 | moved: movd
1211 | moves: movez
1212 | movies: moviez
1213 | moving: movin
1214 | mr: mistah
1215 | much: mutch
1216 | multiuser: multiusr
1217 | my: mah
1218 | naked: nakd
1219 | name: naym
1220 | named: namd
1221 | names: namez
1222 | nation: nashun
1223 | nationwide: nashunwide
1224 | nature: nachur
1225 | need: ned
1226 | needed: needd
1227 | needs: needz
1228 | negotiations: negotiashuns
1229 | neither: neithr
1230 | network: netwerk
1231 | never: nevr
1232 | new: nu
1233 | news: newz
1234 | newsletter: newslettr
1235 | newspaper: nespapr
1236 | nomination: nominashun
1237 | noncelestial: noncelesshul
1238 | nonconfidential: nonconfidenshul
1239 | nondeferential: nondeferenshul
1240 | nonessential: nonessenshul
1241 | nonetheless: nonetheles
1242 | nonevidential: nonevidenshul
1243 | nonexistential: nonexistenshul
1244 | noninitial: noninishul
1245 | nonmartial: nonmarshul
1246 | nonpartial: nonparshul
1247 | nonpotential: nonpotenshul
1248 | nonpreferential: nonpreferenshul
1249 | nonpresidential: nonpresidenshul
1250 | nonprovidential: nonprovidenshul
1251 | nonresidential: nonresidenshul
1252 | nonserviential: nonservienshul
1253 | nonsubstantial: nonsubstanshul
1254 | nontangential: nontangenshul
1255 | notes: notez
1256 | nothing: nothin
1257 | noticed: noticd
1258 | notification: notificashun
1259 | notifications: notificashuns
1260 | noting: notin
1261 | notion: noshun
1262 | november: novembr
1263 | novitial: novishul
1264 | now: nao
1265 | nowadays: nowadais
1266 | nuclear: nucular
1267 | nuggets: nuggetz
1268 | number: numbr
1269 | numbered: numbrd
1270 | numbers: numberz
1271 | nuptial: nupshul
1272 | nutritial: nutrishul
1273 | nytimes: nytimez
1274 | obediential: obedienshul
1275 | objected: objectd
1276 | objections: objecshuns
1277 | obligation: obligashun
1278 | observation: observashun
1279 | observations: observashuns
1280 | observer: observr
1281 | observers: observerz
1282 | obtained: obtaind
1283 | obtaining: obtainin
1284 | obviating: obviatin
1285 | occasional: occashunal
1286 | occasionally: occashunally
1287 | occasions: occashuns
1288 | occurring: occurrin
1289 | october: octobr
1290 | of: ov
1291 | offended: offendd
1292 | offer: offr
1293 | offered: offrd
1294 | offering: offerin
1295 | offers: offerz
1296 | oh: o
1297 | ok: k
1298 | omission: omishun
1299 | one: wan
1300 | ones: onez
1301 | ongoing: ongoin
1302 | opening: openin
1303 | opens: openz
1304 | operating: operatin
1305 | operation: operashun
1306 | operations: operashuns
1307 | opinions: opinionz
1308 | opposed: opposd
1309 | optimized: optimizd
1310 | option: opshun
1311 | optional: opshunal
1312 | optionally: opshunally
1313 | options: opshuns
1314 | order: ordr
1315 | ordered: orderd
1316 | orders: orderz
1317 | oriented: orientd
1318 | ostial: osshul
1319 | other: othr
1320 | others: otherz
1321 | otherwise: otherwize
1322 | our: r
1323 | outliner: outlinr
1324 | outstanding: outstandin
1325 | over: ovar
1326 | overcarged: overchargd
1327 | overheating: overheatin
1328 | overinfluential: overinfluenshul
1329 | overlooked: overlookd
1330 | overpartial: overparshul
1331 | overpotential: overpotenshul
1332 | overpowered: overpowerd
1333 | overrated: overratd
1334 | overrepresented: overrepresentd
1335 | overriding: overridin
1336 | overtures: overturez
1337 | overwhelming: overwhelmin
1338 | owed: owd
1339 | owned: ownd
1340 | owner: ownr
1341 | owners: ownerz
1342 | owns: ownz
1343 | packaging: packagin
1344 | page: paeg
1345 | pages: pagez
1346 | pageviews: pageviewz
1347 | palatial: palashul
1348 | panning: pannin
1349 | pants: pantz
1350 | paper: papr
1351 | paragraph: paragraf
1352 | parallels: parallelz
1353 | parameter: parametr
1354 | parses: parsez
1355 | parsing: parsin
1356 | partial: parshul
1357 | participated: participatd
1358 | partitioning: partitionin
1359 | partners: partnerz
1360 | parts: partz
1361 | pass: pas
1362 | passengers: passengerz
1363 | passes: pasez
1364 | passwords: passwordz
1365 | patents: patentz
1366 | patriots: patriotz
1367 | penitential: penitenshul
1368 | people: peeps
1369 | percent: persent
1370 | perfect: perfik
1371 | performed: performd
1372 | performs: performz
1373 | perhaps: perhapz
1374 | perichaetial: perichaeshul
1375 | pericytial: pericyshul
1376 | peripheral: periferal
1377 | perkins: perkinz
1378 | permission: permishun
1379 | permissions: permishuns
1380 | perplexed: perplexd
1381 | persons: personz
1382 | perspective: perspectiv
1383 | pestilential: pestilenshul
1384 | peter: petr
1385 | phil: fil
1386 | philadelphia: filadelfia
1387 | philip: filip
1388 | phill: fill
1389 | philosophy: filosofy
1390 | phone: fone
1391 | phones: fonez
1392 | photo: foto
1393 | photograph: fotograf
1394 | photographers: fotografers
1395 | photographic: fotografic
1396 | photographs: fotografs
1397 | photonic: fotonic
1398 | photos: fotos
1399 | photoshop: fotoshop
1400 | phrase: frase
1401 | phrasing: frasin
1402 | physical: fysical
1403 | physicist: fysicist
1404 | physics: fysics
1405 | picked: pickd
1406 | picking: pickin
1407 | picks: pickz
1408 | pictures: picturez
1409 | pieces: piecez
1410 | pinching: pinchin
1411 | pirated: piratd
1412 | places: placez
1413 | plagued: plagud
1414 | planning: plannin
1415 | plans: planz
1416 | platforms: platformz
1417 | played: playd
1418 | player: playr
1419 | players: playerz
1420 | playing: playin
1421 | plays: playz
1422 | playstation: playstashun
1423 | please: plz
1424 | pleasing: pleasin
1425 | plenipotential: plenipotenshul
1426 | plugging: pluggin
1427 | plugins: pluginz
1428 | podcasts: podcastz
1429 | pointing: pointin
1430 | points: pointz
1431 | polling: pollin
1432 | pondering: ponderin
1433 | pops: popz
1434 | portals: portalz
1435 | ported: portd
1436 | portion: porshun
1437 | portions: porshuns
1438 | ports: portz
1439 | position: posishun
1440 | positive: positiv
1441 | possession: posesshun
1442 | possibilities: possibilitiez
1443 | possible: posible
1444 | postcomitial: postcomishul
1445 | posted: postd
1446 | poster: postr
1447 | postnuptial: postnupshul
1448 | posts: postz
1449 | potential: potenshul
1450 | pounced: pouncd
1451 | power: powr
1452 | powered: powerd
1453 | praises: praisez
1454 | precedential: precedenshul
1455 | preceding: precedin
1456 | precious: preshus
1457 | predicted: predictd
1458 | predicting: predictin
1459 | prediction: predicshun
1460 | prefatial: prefashul
1461 | prefer: prefr
1462 | preferences: preferencez
1463 | preferential: preferenshul
1464 | preferred: preferrd
1465 | prefs: prefz
1466 | preinitial: preinishul
1467 | prelatial: prelashul
1468 | prenuptial: prenupshul
1469 | preparation: preparashun
1470 | preparations: preparashuns
1471 | prepotential: prepotenshul
1472 | preprocessing: preprocessin
1473 | preputial: prepushul
1474 | presentation: presentashun
1475 | presentations: presentashuns
1476 | presential: presenshul
1477 | presention: presenshun
1478 | president: prezident
1479 | presidential: presidenshul
1480 | press: pres
1481 | pressing: pressin
1482 | presubstantial: presubstanshul
1483 | preteressential: preteressenshul
1484 | preternuptial: preternupshul
1485 | pretty: pritee
1486 | prevented: preventd
1487 | prevents: preventz
1488 | priced: pricd
1489 | prices: pricez
1490 | primaries: primariez
1491 | primatial: primashul
1492 | primitial: primishul
1493 | principles: principlez
1494 | printed: printd
1495 | printing: printin
1496 | priorities: prioritiez
1497 | privileged: privilegd
1498 | privileges: privilegez
1499 | prizes: prizez
1500 | problems: problemz
1501 | proceed: proced
1502 | process: proces
1503 | processing: processin
1504 | proclaiming: proclaimin
1505 | produced: producd
1506 | producer: producr
1507 | produces: producez
1508 | production: producshun
1509 | products: productz
1510 | professional: profeshunal
1511 | professionals: profeshunals
1512 | profressional: profeshunal
1513 | profressionals: profeshunals
1514 | programming: programmin
1515 | progress: progres
1516 | projects: projectz
1517 | promising: promisun
1518 | promoting: promotin
1519 | promotional: promoshunal
1520 | proper: propr
1521 | properties: propertiez
1522 | propitial: propishul
1523 | prospects: prospectz
1524 | protective: protectiv
1525 | protuberantial: protuberanshul
1526 | proves: provez
1527 | provided: providd
1528 | providential: providenshul
1529 | provider: providr
1530 | provides: providez
1531 | providing: providin
1532 | proving: provin
1533 | prudential: prudenshul
1534 | pseudoimpartial: pseudoimparshul
1535 | publication: publicashun
1536 | publications: publicashuns
1537 | publishers: publisherz
1538 | publishes: publishez
1539 | publishing: publishin
1540 | pulled: pulld
1541 | pulling: pullin
1542 | pulls: pullz
1543 | pumping: pumpin
1544 | purchased: purchasd
1545 | purported: purportd
1546 | purposes: purposez
1547 | pursuing: pursuin
1548 | pushed: pushd
1549 | pussy: pusy
1550 | putting: puttin
1551 | puutting: puttin
1552 | quarter: quartr
1553 | quarters: quarterz
1554 | querulential: querulenshul
1555 | question: queshun
1556 | questions: queshuns
1557 | queued: queud
1558 | quicksilver: quicksilvr
1559 | quintessential: quintessenshul
1560 | quitting: quittin
1561 | radioactive: radioactiv
1562 | rails: railz
1563 | raised: raisd
1564 | raises: raisez
1565 | rands: randz
1566 | ranks: rankz
1567 | rather: rathr
1568 | rating: ratin
1569 | reached: reachd
1570 | reaction: reacshun
1571 | reader: readr
1572 | readers: readerz
1573 | reading: readin
1574 | reads: readz
1575 | realizing: realizin
1576 | really: rly
1577 | reasons: reasonz
1578 | recalls: recallz
1579 | receipts: receiptz
1580 | received: receivd
1581 | receiver: receivr
1582 | recent: resent
1583 | reception: recepshun
1584 | recodring: recordin
1585 | recommended: recommendd
1586 | recorder: recordr
1587 | recording: recordin
1588 | recrementitial: recrementishul
1589 | recurring: recurrin
1590 | reducing: reducin
1591 | reduction: reducshun
1592 | referential: referenshul
1593 | referring: referrin
1594 | refusing: refusin
1595 | regarding: regardin
1596 | regardless: regardles
1597 | register: registr
1598 | reinforcing: reinforcin
1599 | rejected: rejectd
1600 | rejection: rejecshun
1601 | related: relatd
1602 | relations: relashuns
1603 | relatives: relativez
1604 | released: releasd
1605 | releases: releasez
1606 | releasing: releasin
1607 | remaining: remainin
1608 | remains: remainz
1609 | remarks: remarkz
1610 | remember: remembr
1611 | reminder: remindr
1612 | reminding: remindin
1613 | reminds: remindz
1614 | reminiscential: reminiscenshul
1615 | removed: removd
1616 | render: rendr
1617 | rendering: rendrin
1618 | renowned: renownd
1619 | rented: rentd
1620 | repairing: repairin
1621 | repeated: repeatd
1622 | repeats: repeatz
1623 | repercussions: repercushuns
1624 | replied: replid
1625 | replies: repliez
1626 | reporing: reportin
1627 | reported: reportd
1628 | reporting: reportin
1629 | reports: reportz
1630 | representatives: representativez
1631 | representing: representin
1632 | requested: requestd
1633 | required: requird
1634 | requirements: requirementz
1635 | requires: requirez
1636 | requiring: requirin
1637 | resentations: presentashuns
1638 | residential: residenshul
1639 | residents: residentz
1640 | resisting: resistn
1641 | resolution: resolushun
1642 | resolved: resolvd
1643 | resources: resourcez
1644 | respected: respectd
1645 | responds: respondz
1646 | responses: responsez
1647 | restless: restles
1648 | restoration: restorashun
1649 | restriction: restricshun
1650 | restrictions: restricshuns
1651 | resulted: resultd
1652 | results: resultz
1653 | retailer: retailr
1654 | retained: retaind
1655 | retention: retenshun
1656 | retial: reshul
1657 | reveals: revealz
1658 | revenues: revenuez
1659 | reverential: reverenshul
1660 | reverses: reversez
1661 | reviewed: reviewd
1662 | reviews: reviewz
1663 | revises: revisez
1664 | revolution: revolushun
1665 | revolutionary: revolushunary
1666 | rewards: rewardz
1667 | rewriting: rewritin
1668 | ridiculous: redikulus
1669 | right: rite
1670 | ripper: rippr
1671 | ripping: rippin
1672 | risks: riskz
1673 | robbed: robbd
1674 | robotics: roboticz
1675 | rodential: rodenshul
1676 | rolling: rollin
1677 | rotation: rotashun
1678 | rows: rowz
1679 | ruffling: rufflin
1680 | rugged: ruggd
1681 | rules: rulez
1682 | rumored: rumord
1683 | rumors: rumorz
1684 | running: runnin
1685 | runs: runz
1686 | said: sed
1687 | sales: salez
1688 | sampling: samplin
1689 | sanctioned: sancshund
1690 | sandwiches: sandwichez
1691 | sans: sanz
1692 | sapiential: sapienshul
1693 | sarbanes: sarbanez
1694 | satisfaction: satisfacshun
1695 | satisfying: satisfyin
1696 | saturday: katurdai
1697 | saving: savin
1698 | savviness: savvines
1699 | say: sai
1700 | saying: sayin
1701 | says: sez
1702 | scaling: scalin
1703 | scared: skerd
1704 | scarification: scarificashun
1705 | scathing: scathin
1706 | scenes: scneez
1707 | scheduled: scheduld
1708 | scheduling: schedulin
1709 | schizophrenia: schizofrenia
1710 | schools: schoolz
1711 | sciential: scienshul
1712 | sclerotial: scleroshul
1713 | score: skor
1714 | scored: scord
1715 | screencasting: screencastin
1716 | screenshots: screenshotz
1717 | screwed: screwd
1718 | screws: screwz
1719 | script: skript
1720 | scripting: scriptin
1721 | scripts: scriptz
1722 | scripture: scripchur
1723 | scrolling: scrollin
1724 | seamless: seamles
1725 | searches: searchez
1726 | searching: searchin
1727 | seating: seatin
1728 | seats: seatz
1729 | seconds: secondz
1730 | secret: seekret
1731 | secrets: seekretz
1732 | section: secshun
1733 | sections: secshuns
1734 | see: c
1735 | seeded: seedd
1736 | seeds: seedz
1737 | seeing: seein
1738 | seeking: seekin
1739 | seeks: seekz
1740 | seemed: seemd
1741 | seems: seemz
1742 | sees: seez
1743 | selected: selectd
1744 | selecting: selectin
1745 | selection: selecshun
1746 | "self's": selfs
1747 | selling: sellin
1748 | sells: sellz
1749 | semicelestial: semicelesshul
1750 | sending: sendin
1751 | sentential: sentenshul
1752 | separation: separashun
1753 | sepbember: septembr
1754 | sequential: sequenshul
1755 | series: seriez
1756 | seriously: srsly
1757 | served: servd
1758 | server: servr
1759 | servers: servrz
1760 | serves: servez
1761 | service: survis
1762 | services: servicez
1763 | serviential: servienshul
1764 | sesquitertial: sesquitershul
1765 | session: seshun
1766 | sessions: seshuns
1767 | settings: settingz
1768 | shared: shard
1769 | shareholder: shareholdr
1770 | shareholders: shareholderz
1771 | shares: sharez
1772 | sharing: sharin
1773 | sheds: shedz
1774 | shipped: shippd
1775 | shipping: shippin
1776 | ships: shipz
1777 | shitting: shittin
1778 | shocking: shockin
1779 | shortcomings: shortcomingz
1780 | shortcuts: shortcutz
1781 | shorter: shortr
1782 | should: shud
1783 | showed: showd
1784 | showing: showin
1785 | shows: showz
1786 | shrinks: shrinkz
1787 | sidelines: sidelinez
1788 | signals: signalz
1789 | signing: signin
1790 | signs: signz
1791 | signups: signupz
1792 | silential: silenshul
1793 | silver: silvr
1794 | sinking: sinkin
1795 | site: siet
1796 | sites: siets
1797 | situation: situashun
1798 | situations: situashuns
1799 | six: "6"
1800 | sized: sizd
1801 | sketching: sketchin
1802 | skewed: skewd
1803 | slides: slidez
1804 | slimmed: slimmd
1805 | slower: slowr
1806 | slumping: slumpin
1807 | smaller: smallr
1808 | smartphone: smartfone
1809 | smells: smellz
1810 | smudged: smudgd
1811 | smuggled: smuggld
1812 | soaked: soakd
1813 | social: soshul
1814 | soldering: solderin
1815 | solstitial: solstishul
1816 | solution: solushun
1817 | solutions: solushuns
1818 | solved: solvd
1819 | some: sum
1820 | something: somethin
1821 | sometimes: sometimez
1822 | sophisticated: sofisticatd
1823 | sorry: sry
1824 | sounding: soundin
1825 | sounds: soundz
1826 | sources: sourcez
1827 | sourcing: sourcin
1828 | spaces: spacez
1829 | spangled: spangld
1830 | spatial: spashul
1831 | speakers: speakerz
1832 | speaking: speakin
1833 | specced: speccd
1834 | special: speshul
1835 | specialized: specializd
1836 | specification: specificashun
1837 | specifications: specificashuns
1838 | specs: specz
1839 | spectators: spectatorz
1840 | specter: spectr
1841 | speculating: speculatin
1842 | speculation: speculashun
1843 | specutlitve: speculativ
1844 | speed: sped
1845 | spends: spendz
1846 | spoilers: spoilerz
1847 | sponsoring: sponsorin
1848 | sporting: sportin
1849 | spreading: spreadin
1850 | spreads: spreadz
1851 | spreadsheets: spreadsheetz
1852 | sprinkles: sprinklez
1853 | squeezed: squeezd
1854 | stacks: stackz
1855 | standardized: standardizd
1856 | standards: standardz
1857 | stands: standz
1858 | starbucks: starbuckz
1859 | stars: starz
1860 | started: startd
1861 | starting: startin
1862 | starts: startz
1863 | stashing: stashin
1864 | stated: statd
1865 | states: statez
1866 | station: stashun
1867 | stations: stashuns
1868 | stats: statz
1869 | staying: stayin
1870 | stays: stayz
1871 | steal: steel
1872 | stealing: stealin
1873 | stephenson: stefenson
1874 | stepped: steppd
1875 | steps: stepz
1876 | sticking: stickin
1877 | stocked: stockd
1878 | stocks: stockz
1879 | stored: stord
1880 | stores: storez
1881 | stories: storiez
1882 | strained: straind
1883 | strapped: strappd
1884 | strategies: strategiez
1885 | streaming: streamin
1886 | streamlined: streamlind
1887 | strengths: strengthz
1888 | strikes: strikez
1889 | striking: strikin
1890 | string: strin
1891 | strips: stripz
1892 | stronger: strongr
1893 | structure: strucchur
1894 | struggled: struggld
1895 | students: studentz
1896 | stunning: stunnin
1897 | subcelestial: subcelesshul
1898 | subessential: subessenshul
1899 | subinitial: subinishul
1900 | subintelligential: subintelligenshul
1901 | submissions: submishuns
1902 | subscribers: subscriberz
1903 | subscription: subscripshun
1904 | subsequential: subsequenshul
1905 | subsistential: subsistenshul
1906 | substantial: substanshul
1907 | substring: substrin
1908 | subtercelestial: subtercelesshul
1909 | subversion: subvershun
1910 | succeed: succed
1911 | succeeds: succeedz
1912 | success: succes
1913 | sucks: suckz
1914 | suffer: suffr
1915 | suffers: sufferz
1916 | suggested: suggestd
1917 | suggestions: sugesshuns
1918 | suggests: suggestz
1919 | suited: suitd
1920 | summarizes: summarizez
1921 | super: supr
1922 | supercelestial: supercelesshul
1923 | superduper: superdupr
1924 | superessential: superessenshul
1925 | superintendential: superintendenshul
1926 | supersesquitertial: supersesquitershul
1927 | supersubstantial: supersubstanshul
1928 | supplied: supplid
1929 | suppliers: supplierz
1930 | supported: supportd
1931 | supporters: supporterz
1932 | supports: supportz
1933 | supposed: supposd
1934 | suprise: surprize
1935 | surfing: surfin
1936 | surprised: surprisd
1937 | surprises: surprisez
1938 | surprising: surprisn
1939 | surrounding: surroundin
1940 | sutied: suitd
1941 | sweater: sweatr
1942 | swelled: swelld
1943 | switched: switchd
1944 | switches: switchez
1945 | switching: switchin
1946 | swizzling: swizzlin
1947 | synching: synchin
1948 | syncytial: syncyshul
1949 | system: sistem
1950 | sytems: sistemz
1951 | tagging: taggin
1952 | take: taek
1953 | takeover: takeovr
1954 | takes: takez
1955 | taking: takin
1956 | takover: takeovr
1957 | talented: talentd
1958 | talked: talkd
1959 | talking: talkin
1960 | talks: talkz
1961 | tangential: tangenshul
1962 | tapes: tapez
1963 | taping: tapin
1964 | targeting: targetin
1965 | targets: targetz
1966 | tasks: taskz
1967 | tasting: tastin
1968 | teaching: teachin
1969 | teams: teamz
1970 | techniques: techniquez
1971 | technologies: technologiez
1972 | telephotos: telefotoz
1973 | telestial: telesshul
1974 | telling: tellin
1975 | tendential: tendenshul
1976 | teretial: tereshul
1977 | terms: termz
1978 | tertial: tershul
1979 | tester: testr
1980 | testing: testin
1981 | tests: testz
1982 | textwrangler: textwranglr
1983 | thanks: thx
1984 | that: dat
1985 | the: teh
1986 | theater: theatr
1987 | their: der
1988 | themselves: themselvez
1989 | there: thar
1990 | these: thees
1991 | thing: ting
1992 | things: tings
1993 | think: finks
1994 | thinking: thinkin
1995 | thinkpads: thinkpadz
1996 | thinnes: thinnes
1997 | this: dis
1998 | those: dose
1999 | thought: thot
2000 | thoughts: thoughtz
2001 | thousands: thousandz
2002 | three: "3"
2003 | thrives: thrivez
2004 | thriving: thrivin
2005 | through: thru
2006 | throwing: throwin
2007 | thursday: thursdai
2008 | tiered: tierd
2009 | tiering: tierin
2010 | tiles: tilez
2011 | time: tiem
2012 | times: tiems
2013 | timing: timin
2014 | tinker: tinkr
2015 | titles: titlez
2016 | to: "2"
2017 | today: todai
2018 | together: togehtr
2019 | tomorrow: "2morrow"
2020 | tonight: tonite
2021 | too: "2"
2022 | tools: toolz
2023 | topics: topicz
2024 | torrential: torrenshul
2025 | tossup: tosup
2026 | totipotential: totipotenshul
2027 | touches: touchez
2028 | touted: toutd
2029 | towards: towardz
2030 | tower: towr
2031 | toys: toyz
2032 | tracking: trackin
2033 | trackpads: trackpadz
2034 | tracks: trackz
2035 | traction: tracshun
2036 | trademarks: trademarkz
2037 | tradional: tradishunal
2038 | trafficked: traffickd
2039 | transaction: transacshun
2040 | transcript: tranzscript
2041 | transexperiential: transexperienshul
2042 | transfer: transfr
2043 | transferential: transferenshul
2044 | transformation: transformashun
2045 | transition: transishun
2046 | translate: tranzlate
2047 | translated: translatd
2048 | translation: translashun
2049 | translator: tranzlator
2050 | transmitted: transmittd
2051 | transubstantial: transubstanshul
2052 | treated: treatd
2053 | trekked: trekkd
2054 | triangulation: triangulashun
2055 | tribunitial: tribunishul
2056 | tricks: trickz
2057 | tried: trid
2058 | triggering: triggerin
2059 | trimming: trimmin
2060 | trinoctial: trinocshul
2061 | tritangential: tritangenshul
2062 | troubled: troubld
2063 | troubling: troublin
2064 | trying: tryin
2065 | tuesday: tuesdai
2066 | turned: turnd
2067 | turns: turnz
2068 | tweaking: tweakin
2069 | tweeted: tweetd
2070 | tweets: tweetz
2071 | twitter: twittr
2072 | two: "2"
2073 | tying: tyin
2074 | typing: typin
2075 | typograhy: tyopgrafy
2076 | typography: typografy
2077 | unanchored: unanchord
2078 | unanswered: unanzwerd
2079 | unapproved: unapprovd
2080 | unboxing: unboxin
2081 | uncelestial: uncelesshul
2082 | uncircumstantial: uncircumstanshul
2083 | unconfidential: unconfidenshul
2084 | unconsequential: unconsequenshul
2085 | undefeated: undefeatd
2086 | undeferential: undeferenshul
2087 | under: undr
2088 | underlying: underlyin
2089 | underpowered: underpowerd
2090 | underscores: underscorez
2091 | understanding: understandin
2092 | undertaking: undertakin
2093 | undervalued: undervalud
2094 | undifferential: undifferenshul
2095 | unessential: unessenshul
2096 | unevidential: unevidenshul
2097 | unexpected: unexpectd
2098 | unexperiential: unexperienshul
2099 | unimpartial: unimparshul
2100 | uninfluential: uninfluenshul
2101 | unipotential: unipotenshul
2102 | united: unitd
2103 | units: unitz
2104 | unless: unles
2105 | unlocked: unlockd
2106 | unlocking: unlockin
2107 | unmartial: unmarshul
2108 | unnamed: unnamd
2109 | unpalatial: unpalashul
2110 | unpartial: unparshul
2111 | unpestilential: unpestilenshul
2112 | unprecedential: unprecedenshul
2113 | unpresidential: unpresidenshul
2114 | unprovidential: unprovidenshul
2115 | unreverential: unreverenshul
2116 | unsequential: unsequenshul
2117 | unsolicited: unsolicitd
2118 | unspatial: unspashul
2119 | unsubstantial: unsubstanshul
2120 | untangential: untangenshul
2121 | until: til
2122 | unveiled: unveild
2123 | unwise: unwize
2124 | upcoming: upcomin
2125 | updated: updatd
2126 | updates: updatez
2127 | upgraded: upgradd
2128 | upgrades: upgradez
2129 | uploads: uploadz
2130 | upper: uppr
2131 | upscaled: upscald
2132 | used: usd
2133 | useless: useles
2134 | user: usr
2135 | users: userz
2136 | uses: usez
2137 | using: usin
2138 | utilities: utilitiez
2139 | utter: uttr
2140 | vader: vadr
2141 | variables: variablez
2142 | vending: vendin
2143 | ventripotential: ventripotenshul
2144 | version: vershun
2145 | versionable: vershunable
2146 | versions: vershuns
2147 | very: vary
2148 | videos: videoz
2149 | videotapes: videotapez
2150 | videotaping: videotapin
2151 | viewed: viewd
2152 | viewers: viewerz
2153 | vindicated: vindicatd
2154 | violations: violashuns
2155 | virtualization: virtualizashun
2156 | vision: vishun
2157 | visionless: vishunles
2158 | visions: vishuns
2159 | visited: visietd
2160 | visualization: visualizashun
2161 | vivisectin: vivisecshun
2162 | voters: voterz
2163 | voting: votin
2164 | vowed: vowd
2165 | waiting: waitin
2166 | waking: wakin
2167 | want: wants
2168 | wanted: wantd
2169 | wanting: wantin
2170 | wants: wantz
2171 | warming: warmin
2172 | warned: warnd
2173 | warner: warnr
2174 | warranties: warrantiez
2175 | wars: warz
2176 | was: wuz
2177 | watching: watchin
2178 | watered: waterd
2179 | way: wai
2180 | ways: wayz
2181 | "we'll": well
2182 | "we're": were
2183 | weakness: weaknes
2184 | wearing: wearin
2185 | weather: weathr
2186 | webapps: webappz
2187 | website: websiet
2188 | websites: websiets
2189 | weekday: weekdai
2190 | weeks: weekz
2191 | welcome: welcom
2192 | were: wuz
2193 | "weren't": "wuz not"
2194 | what: wut
2195 | whatever: whatevr
2196 | whatsoever: whatsoevr
2197 | whenever: whenevr
2198 | where: wer
2199 | whether: whethr
2200 | whipped: whippd
2201 | who: hoo
2202 | whoever: whoevr
2203 | why: y
2204 | widgets: widgetz
2205 | wiggling: wigglin
2206 | willing: willin
2207 | windows: windowz
2208 | winner: winnr
2209 | winners: winnerz
2210 | winning: winnin
2211 | wins: winz
2212 | wired: wird
2213 | wished: wishd
2214 | wishes: wishez
2215 | with: wif
2216 | wnated: wantd
2217 | wonder: wondr
2218 | wondering: wonderin
2219 | word: werd
2220 | wordpress: werdpres
2221 | words: werdz
2222 | work: werk
2223 | workaround: werkaround
2224 | worked: werkd
2225 | worker: werkr
2226 | working: werkin
2227 | works: werkz
2228 | workstation: workstashun
2229 | workstations: werkstashuns
2230 | world: wurld
2231 | worst: wurst
2232 | worth: wurth
2233 | would: wud
2234 | write: rite
2235 | writer: writr
2236 | writes: writez
2237 | writing: writin
2238 | written: writteh
2239 | yards: yardz
2240 | yeah: ya
2241 | year: yer
2242 | years: yeers
2243 | "yes": yez
2244 | yesterday: yesterdai
2245 | you: u
2246 | "you'd": ud
2247 | younger: youngr
2248 | your: ur
2249 | yours: urz
2250 | zoocytial: zoocyshul
2251 | zoomer: zoomr
2252 | zooming: zommin
2253 | 


--------------------------------------------------------------------------------
/Chapter 10/NLTK Chapter 10 - Notes .ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "# NLTK Chapter 10\n",
 8 |     "\n",
 9 |     "## Analyzing the Meaning of Sentences\n",
10 |     "\n",
11 |     "*The html version of this chapter in the book is available [here](https://www.nltk.org/book/ch10.html \"ch10\").*\n",
12 |     "\n",
13 |     "__*I skipped this chapter, as I have scant experience with propositional logic, and didn't feel that any of the techniques therein had any practical value for me.*__"
14 |    ]
15 |   }
16 |  ],
17 |  "metadata": {
18 |   "kernelspec": {
19 |    "display_name": "Python 3",
20 |    "language": "python",
21 |    "name": "python3"
22 |   },
23 |   "language_info": {
24 |    "codemirror_mode": {
25 |     "name": "ipython",
26 |     "version": 3
27 |    },
28 |    "file_extension": ".py",
29 |    "mimetype": "text/x-python",
30 |    "name": "python",
31 |    "nbconvert_exporter": "python",
32 |    "pygments_lexer": "ipython3",
33 |    "version": "3.7.3"
34 |   }
35 |  },
36 |  "nbformat": 4,
37 |  "nbformat_minor": 2
38 | }
39 | 


--------------------------------------------------------------------------------
/Chapter 11/Swadesh Lists.csv:
--------------------------------------------------------------------------------
  1 | i,English,je,French,ich,German,io,Italian,ego,Latin,ja,Russian,yo,Spanish,ik,Dutch,jag,Swedish,ahám,Sanskrit
  2 | you,English,tu,French,du,German,voi,Italian,vos,Latin,vy,Russian,usted,Spanish,jij,Dutch,du,Swedish,tvám,Sanskrit
  3 | thou,English,vous ,French,sie,German,tu,Italian,tu,Latin,ty,Russian,tú,Spanish,u,Dutch,du,Swedish,tvám,Sanskrit
  4 | he,English,il,French,er,German,egli,Italian,ipse,Latin,on,Russian,él,Spanish,hij,Dutch,han,Swedish,sá,Sanskrit
  5 | we,English,nous,French,wir,German,noi,Italian,nos,Latin,my,Russian,nosotros,Spanish,wij,Dutch,vi,Swedish,vayám,Sanskrit
  6 | you,English,vous,French,ihr,German,voi,Italian,vos,Latin,vy,Russian,usted,Spanish,jullie,Dutch,ni,Swedish,yūyám,Sanskrit
  7 | they,English,ils,French,sie,German,essi,Italian,ipsi,Latin,oni,Russian,ellos,Spanish,zij,Dutch,de,Swedish,té,Sanskrit
  8 | this,English,ceci,French,dieses,German,questo,Italian,hoc,Latin,e'tot,Russian,esta,Spanish,deze,Dutch,det här,Swedish,idám,Sanskrit
  9 | that,English,cela,French,jenes,German,che,Italian,quod,Latin,tot,Russian,que,Spanish,die,Dutch,det där,Swedish,tát,Sanskrit
 10 | here,English,ici,French,hier,German,qui,Italian,hic,Latin,zdes',Russian,aquí,Spanish,hier,Dutch,här,Swedish,átra,Sanskrit
 11 | there,English,là,French,dort,German,ci,Italian,ibi,Latin,tam,Russian,hay,Spanish,daar,Dutch,där,Swedish,tátra,Sanskrit
 12 | who,English,qui,French,wer,German,che,Italian,qui,Latin,kto,Russian,que,Spanish,wie,Dutch,vem,Swedish,ká,Sanskrit
 13 | what,English,quoi,French,was,German,cosa,Italian,quid,Latin,chto,Russian,¿qué,Spanish,wat,Dutch,vad,Swedish,kím,Sanskrit
 14 | where,English,où,French,wo,German,dove,Italian,ubi,Latin,gde,Russian,donde,Spanish,waar,Dutch,var,Swedish,kútra,Sanskrit
 15 | when,English,quand,French,wann,German,quando,Italian,quando,Latin,kogda,Russian,cuando,Spanish,wanneer,Dutch,när,Swedish,kadā́,Sanskrit
 16 | how,English,comment,French,wie,German,come,Italian,quomodo,Latin,kak,Russian,cómo,Spanish,hoe,Dutch,hur,Swedish,kathám,Sanskrit
 17 | not,English,ne...pas,French,nicht,German,non,Italian,non,Latin,ne,Russian,no,Spanish,niet,Dutch,inte,Swedish,ná,Sanskrit
 18 | all,English,tout,French,alle,German,tutti,Italian,omnes,Latin,vse,Russian,todos,Spanish,al,Dutch,alla,Swedish,sárva,Sanskrit
 19 | many,English,plusieurs,French,viele,German,molti,Italian,multis,Latin,mnogo,Russian,muchos,Spanish,veel,Dutch,många,Swedish,bahú,Sanskrit
 20 | some,English,quelques,French,einige,German,un po 'di,Italian,aliqua,Latin,nekotorye,Russian,algunos,Spanish,enkele,Dutch,några,Swedish,kiñcid,Sanskrit
 21 | few,English,peu,French,wenige,German,pochi,Italian,pauci,Latin,malo,Russian,pocos,Spanish,weinig,Dutch,få,Swedish,álpa,Sanskrit
 22 | other,English,autre,French,andere,German,altro,Italian,aliis,Latin,drugoj,Russian,otro,Spanish,ander,Dutch,annan,Swedish,anyá,Sanskrit
 23 | one,English,un,French,eins,German,uno,Italian,unum,Latin,odin,Russian,uno,Spanish,een,Dutch,ett,Swedish,éka,Sanskrit
 24 | two,English,deux,French,zwei,German,due,Italian,duo,Latin,dva,Russian,dos,Spanish,twee,Dutch,två,Swedish,dví,Sanskrit
 25 | three,English,trois,French,drei,German,tre,Italian,tres,Latin,tri,Russian,tres,Spanish,drie,Dutch,tre,Swedish,trí,Sanskrit
 26 | four,English,quatre,French,vier,German,quattro,Italian,quattuor,Latin,chetyre,Russian,cuatro,Spanish,vier,Dutch,fyra,Swedish,cátur,Sanskrit
 27 | five,English,cinq,French,fünf,German,cinque,Italian,quinque,Latin,pjat',Russian,cinco,Spanish,vijf,Dutch,fem,Swedish,páñcan,Sanskrit
 28 | big,English,grand,French,groß,German,grande,Italian,magnus,Latin,bol'shoj,Russian,grande,Spanish,groot,Dutch,stor,Swedish,mahát,Sanskrit
 29 | long,English,long,French,lang,German,lungo,Italian,diu,Latin,dlinnyj,Russian,largo,Spanish,lang,Dutch,lång,Swedish,dīrghá,Sanskrit
 30 | wide,English,large,French,breit,German,ampio,Italian,lata,Latin,shirokij,Russian,ancho,Spanish,breed,Dutch,bred,Swedish,urú,Sanskrit
 31 | thick,English,épais,French,dick,German,spesso,Italian,crassus,Latin,tolstyj,Russian,espesor,Spanish,dik,Dutch,tjock,Swedish,ghaná,Sanskrit
 32 | heavy,English,lourd,French,schwer,German,pesante,Italian,gravis,Latin,tjazhjolyj,Russian,pesado,Spanish,zwaar,Dutch,tung,Swedish,gurú,Sanskrit
 33 | small,English,petit,French,klein,German,piccolo,Italian,parva,Latin,malen'kij,Russian,pequeño,Spanish,klein,Dutch,liten,Swedish,álpa,Sanskrit
 34 | short,English,court,French,kurz,German,breve,Italian,brevis,Latin,korotkij,Russian,corto,Spanish,kort,Dutch,kort,Swedish,hrasvá,Sanskrit
 35 | narrow,English,étroit,French,eng,German,stretto,Italian,angustus,Latin,uzkij,Russian,estrecho,Spanish,smal,Dutch,trång,Swedish,aṃhú,Sanskrit
 36 | thin,English,mince,French,dünn,German,sottile,Italian,tenues,Latin,tonkij,Russian,delgado,Spanish,dun,Dutch,tunn,Swedish,tanú,Sanskrit
 37 | woman,English,femme,French,frau,German,donna,Italian,mulier,Latin,zhenschina,Russian,mujer,Spanish,vrouw,Dutch,kvinna,Swedish,strī́,Sanskrit
 38 | man,English,homme,French,mann,German,uomo,Italian,homo,Latin,muzhchina,Russian,hombre,Spanish,man,Dutch,man,Swedish,púruṣa,Sanskrit
 39 | man,English,homme,French,mensch,German,uomo,Italian,homo,Latin,chelovek,Russian,hombre,Spanish,mens,Dutch,människa,Swedish,manuṣyá,Sanskrit
 40 | child,English,enfant,French,kind,German,bambino,Italian,puer,Latin,rebjonok,Russian,niño,Spanish,kind,Dutch,barn,Swedish,bā́la,Sanskrit
 41 | wife,English,femme,French,frau,German,moglie,Italian,uxor,Latin,zhena,Russian,esposa,Spanish,vrouw,Dutch,hustru,Swedish,pátnī,Sanskrit
 42 | husband,English,mari,French,mann,German,marito,Italian,maritus,Latin,muzh,Russian,marido,Spanish,man,Dutch,man,Swedish,páti,Sanskrit
 43 | mother,English,mère,French,mutter,German,madre,Italian,mater,Latin,mat',Russian,madre,Spanish,moeder,Dutch,mamma,Swedish,mā́tṛ,Sanskrit
 44 | father,English,père,French,vater,German,padre,Italian,pater,Latin,otec,Russian,padre,Spanish,vader,Dutch,pappa,Swedish,pitṛ́,Sanskrit
 45 | animal,English,animal,French,tier,German,animale,Italian,animalis,Latin,zhivotnoe,Russian,animal,Spanish,dier,Dutch,djur,Swedish,páśu,Sanskrit
 46 | fish,English,poisson,French,fisch,German,pesce,Italian,piscis,Latin,ryba,Russian,pescado,Spanish,vis,Dutch,fisk,Swedish,mátsya,Sanskrit
 47 | bird,English,oiseau,French,vogel,German,uccello,Italian,avis,Latin,ptica,Russian,pájaro,Spanish,vogel,Dutch,fågel,Swedish,ví,Sanskrit
 48 | dog,English,chien,French,hund,German,cane,Italian,canis,Latin,sobaka,Russian,perro,Spanish,hond,Dutch,hund,Swedish,śván,Sanskrit
 49 | louse,English,pou,French,laus,German,pidocchio,Italian,pedis,Latin,vosh',Russian,piojo,Spanish,luis,Dutch,lus,Swedish,yūkā,Sanskrit
 50 | snake,English,serpent,French,schlange,German,serpente,Italian,anguis,Latin,zmeja,Russian,serpiente,Spanish,slang,Dutch,orm,Swedish,sarpá,Sanskrit
 51 | worm,English,ver,French,wurm,German,verme,Italian,vermis,Latin,cherv',Russian,gusano,Spanish,worm,Dutch,mask,Swedish,kṛ́mi,Sanskrit
 52 | tree,English,arbre,French,baum,German,albero,Italian,arbor,Latin,derevo,Russian,árbol,Spanish,boom,Dutch,träd,Swedish,vṛkṣá,Sanskrit
 53 | forest,English,forêt,French,wald,German,foresta,Italian,silva,Latin,les,Russian,bosque,Spanish,woud,Dutch,skog,Swedish,vána,Sanskrit
 54 | stick,English,bâton,French,stock,German,attaccare,Italian,inhaero,Latin,palka,Russian,palo,Spanish,stok,Dutch,pinne,Swedish,daṇḍá,Sanskrit
 55 | fruit,English,fruit,French,frucht,German,frutta,Italian,fructus,Latin,plod,Russian,fruta,Spanish,fruit,Dutch,frukt,Swedish,phála,Sanskrit
 56 | seed,English,graine,French,samen,German,seme,Italian,semen,Latin,semja,Russian,semilla,Spanish,zaad,Dutch,frö,Swedish,bī́ja,Sanskrit
 57 | leaf,English,feuille,French,blatt,German,foglia,Italian,folium,Latin,list,Russian,hoja,Spanish,blad,Dutch,löv,Swedish,páttra,Sanskrit
 58 | root,English,racine,French,wurzel,German,radice,Italian,radix,Latin,koren',Russian,raíz,Spanish,wortel,Dutch,rot,Swedish,mū́la,Sanskrit
 59 | bark,English,écorce,French,rinde,German,corteccia,Italian,cortex,Latin,kora,Russian,ladrido,Spanish,schors,Dutch,bark,Swedish,tvác,Sanskrit
 60 | flower,English,fleur,French,blume,German,fiore,Italian,flos,Latin,cvetok,Russian,flor,Spanish,bloem,Dutch,blomma,Swedish,púṣpa,Sanskrit
 61 | grass,English,herbe,French,gras,German,erba,Italian,herba,Latin,trava,Russian,hierba,Spanish,gras,Dutch,gräs,Swedish,tṛ́ṇa,Sanskrit
 62 | rope,English,corde,French,seil,German,corda,Italian,funis,Latin,verjovka,Russian,cuerda,Spanish,touw,Dutch,rep,Swedish,rájju,Sanskrit
 63 | skin,English,peau,French,haut,German,pelle,Italian,cutis,Latin,kozha,Russian,piel,Spanish,huid,Dutch,hud,Swedish,cárman,Sanskrit
 64 | meat,English,viande,French,fleisch,German,carne,Italian,cibus,Latin,mjaso,Russian,carne,Spanish,vlees,Dutch,kött,Swedish,māṃsá,Sanskrit
 65 | blood,English,sang,French,blut,German,sangue,Italian,sanguis,Latin,krov',Russian,sangre,Spanish,bloed,Dutch,blod,Swedish,rakta,Sanskrit
 66 | bone,English,os,French,knochen,German,osso,Italian,os,Latin,kost',Russian,hueso,Spanish,been,Dutch,ben,Swedish,ásthi,Sanskrit
 67 | fat,English,graisse,French,fett,German,grasso,Italian,adipem,Latin,zhir,Russian,grasa,Spanish,vet,Dutch,fett,Swedish,pī́vas,Sanskrit
 68 | egg,English,œuf,French,ei,German,uovo,Italian,ovum,Latin,jajco,Russian,huevo,Spanish,ei,Dutch,ägg,Swedish,aṇḍá,Sanskrit
 69 | horn,English,corne,French,horn,German,corno,Italian,cornu,Latin,rog,Russian,cuerno,Spanish,hoorn,Dutch,horn,Swedish,śṛṅgá,Sanskrit
 70 | tail,English,queue,French,schwanz,German,coda,Italian,cauda,Latin,hvost,Russian,cola,Spanish,staart,Dutch,svans,Swedish,púccha,Sanskrit
 71 | feather,English,plume,French,feder,German,piuma,Italian,pinnam,Latin,pero,Russian,pluma,Spanish,veer,Dutch,fjäder,Swedish,parṇá,Sanskrit
 72 | hair,English,cheveu,French,haar,German,capelli,Italian,capillus,Latin,volos,Russian,cabello,Spanish,haar,Dutch,hår,Swedish,kéśa,Sanskrit
 73 | head,English,tête,French,kopf ,German,testa,Italian,caput,Latin,golova,Russian,cabeza,Spanish,hoofd,Dutch,huvud,Swedish,śíras,Sanskrit
 74 | ear,English,oreille,French,ohr,German,orecchio,Italian,auris,Latin,uho,Russian,oído,Spanish,oor,Dutch,öra,Swedish,kárṇa,Sanskrit
 75 | eye,English,œil,French,auge,German,occhio,Italian,oculus,Latin,glaz,Russian,ojo,Spanish,oog,Dutch,öga,Swedish,ákṣi,Sanskrit
 76 | nose,English,nez,French,nase,German,naso,Italian,nasus,Latin,nos,Russian,nariz,Spanish,neus,Dutch,näsa,Swedish,nā́sā,Sanskrit
 77 | mouth,English,bouche,French,mund,German,bocca,Italian,os,Latin,rot,Russian,boca,Spanish,mond,Dutch,mun,Swedish,vaktra,Sanskrit
 78 | tooth,English,dent,French,zahn,German,dente,Italian,dentem,Latin,zub,Russian,diente,Spanish,tand,Dutch,tand,Swedish,dánta,Sanskrit
 79 | tongue,English,langue,French,zunge,German,lingua,Italian,lingua,Latin,jazyk,Russian,lengua,Spanish,tong,Dutch,tunga,Swedish,jihvā́,Sanskrit
 80 | fingernail,English,ongle,French,fingernagel,German,unghia,Italian,unguem,Latin,nogot',Russian,uña,Spanish,vingernagel,Dutch,nagel,Swedish,nakhá,Sanskrit
 81 | foot,English,pied,French,fuß,German,piede,Italian,pes,Latin,stupnja,Russian,pie,Spanish,voet,Dutch,fot,Swedish,padá,Sanskrit
 82 | leg,English,jambe,French,bein,German,gamba,Italian,crus,Latin,noga,Russian,pierna,Spanish,been,Dutch,ben,Swedish,jáṅgha,Sanskrit
 83 | knee,English,genou,French,knie,German,ginocchio,Italian,genu,Latin,koleno,Russian,rodilla,Spanish,knie,Dutch,knä,Swedish,jā́nu,Sanskrit
 84 | hand,English,main,French,hand,German,mano,Italian,manu,Latin,kist' ruki,Russian,mano,Spanish,hand,Dutch,hand,Swedish,hásta,Sanskrit
 85 | wing,English,aile,French,flügel,German,ala,Italian,ala,Latin,krylo,Russian,ala,Spanish,vleugel,Dutch,vinge,Swedish,pakṣá,Sanskrit
 86 | belly,English,ventre,French,bauch,German,pancia,Italian,venter,Latin,zhivot,Russian,vientre,Spanish,buik,Dutch,mage,Swedish,udára,Sanskrit
 87 | guts,English,entrailles,French,eingeweide,German,budella,Italian,viscera,Latin,kishki,Russian,agallas,Spanish,ingewanden,Dutch,inälvor,Swedish,ántra,Sanskrit
 88 | neck,English,cou,French,hals,German,collo,Italian,collum,Latin,sheja,Russian,cuello,Spanish,nek,Dutch,hals,Swedish,gala,Sanskrit
 89 | back,English,dos,French,rücken,German,indietro,Italian,retro,Latin,spina,Russian,espalda,Spanish,rug,Dutch,rygg,Swedish,pṛṣṭhá,Sanskrit
 90 | breast,English,sein,French,brust,German,seno,Italian,pectus,Latin,grud',Russian,pecho,Spanish,borst,Dutch,bröst,Swedish,stána,Sanskrit
 91 | heart,English,cœur,French,herz,German,cuore,Italian,cor,Latin,serdce,Russian,corazón,Spanish,hart,Dutch,hjärta,Swedish,hṛ́daya,Sanskrit
 92 | liver,English,foie,French,leber,German,fegato,Italian,iecur,Latin,pechen',Russian,hígado,Spanish,lever,Dutch,lever,Swedish,yákṛt,Sanskrit
 93 | drink,English,boire,French,trinken,German,bere,Italian,bibere,Latin,pit',Russian,beber,Spanish,drinken,Dutch,dricka,Swedish,píbati,Sanskrit
 94 | eat,English,manger,French,essen,German,mangiare,Italian,manducare,Latin,est',Russian,comer,Spanish,eten,Dutch,äta,Swedish,khā́dati,Sanskrit
 95 | bite,English,mordre,French,beißen,German,mordere,Italian,mordere,Latin,kusat',Russian,morder,Spanish,bijten,Dutch,bita,Swedish,dáśati,Sanskrit
 96 | suck,English,sucer,French,saugen,German,succhiare,Italian,nutrientibus,Latin,sosat',Russian,chupar,Spanish,zuigen,Dutch,suga,Swedish,dháyati,Sanskrit
 97 | spit,English,cracher,French,spucken,German,sputare,Italian,conspuentibus,Latin,plevat',Russian,escupir,Spanish,spuwen,Dutch,spotta,Swedish,ṣṭhī́vati,Sanskrit
 98 | vomit,English,vomir,French,erbrechen,German,vomitare,Italian,evomere,Latin,blevat',Russian,vomitar,Spanish,braken,Dutch,kräkas,Swedish,vámati,Sanskrit
 99 | blow,English,souffler,French,blasen,German,soffiare,Italian,flarent,Latin,dut',Russian,soplar,Spanish,blazen,Dutch,blåsa,Swedish,vā́ti,Sanskrit
100 | breathe,English,respirer,French,atmen,German,respirare,Italian,spirant,Latin,dyshat',Russian,respirar,Spanish,ademen,Dutch,andas,Swedish,ániti,Sanskrit
101 | laugh,English,rire,French,lachen,German,ridere,Italian,"ridere,",Latin,smejat'sja,Russian,reír,Spanish,lachen,Dutch,skratta,Swedish,smáyate,Sanskrit
102 | see,English,voir,French,sehen,German,vedere,Italian,videre,Latin,videt',Russian,ver,Spanish,zien,Dutch,se,Swedish,páśyati,Sanskrit
103 | hear,English,entendre,French,hören,German,sentire,Italian,audire,Latin,slyshat',Russian,escuchar,Spanish,horen,Dutch,höra,Swedish,śṛṇóti,Sanskrit
104 | know,English,savoir,French,wissen,German,sapere,Italian,scire,Latin,znat',Russian,saber,Spanish,weten,Dutch,veta,Swedish,jānā́ti,Sanskrit
105 | think,English,penser,French,denken,German,pensare,Italian,cogitare,Latin,dumat',Russian,pensar,Spanish,denken,Dutch,tänka,Swedish,mányate,Sanskrit
106 | smell,English,sentir,French,riechen,German,odore,Italian,odor,Latin,njuhat',Russian,olor,Spanish,ruiken,Dutch,lukta,Swedish,jíghrati,Sanskrit
107 | fear,English,craindre,French,fürchten,German,paura,Italian,timor,Latin,bojat'sja,Russian,miedo,Spanish,vrezen,Dutch,frukta,Swedish,bibhéti,Sanskrit
108 | sleep,English,dormir,French,schlafen,German,dormire,Italian,somnus,Latin,spat',Russian,dormir,Spanish,slapen,Dutch,sova,Swedish,svápiti,Sanskrit
109 | live,English,vivre,French,leben,German,vivere,Italian,vivere,Latin,zhit',Russian,vivir,Spanish,leven,Dutch,leva,Swedish,jī́vati,Sanskrit
110 | die,English,mourir,French,sterben,German,morire,Italian,mori,Latin,umirat',Russian,morir,Spanish,sterven,Dutch,dö,Swedish,mriyáte,Sanskrit
111 | kill,English,tuer,French,töten,German,uccidere,Italian,occidere,Latin,ubivat',Russian,matar,Spanish,doden,Dutch,döda,Swedish,hánti,Sanskrit
112 | fight,English,se battre,French,kämpfen,German,combattere,Italian,pugnare,Latin,borot'sja,Russian,luchar,Spanish,vechten,Dutch,strida,Swedish,yúdhyate,Sanskrit
113 | hunt,English,chasser,French,jagen,German,caccia,Italian,hunt,Latin,ohotit'sja,Russian,caza,Spanish,jagen,Dutch,jaga,Swedish,veti,Sanskrit
114 | hit,English,frapper,French,schlagen,German,colpire,Italian,ledo,Latin,bit',Russian,golpear,Spanish,slaan,Dutch,slå,Swedish,hánti,Sanskrit
115 | cut,English,couper,French,schneiden,German,taglio,Italian,secare,Latin,rezat',Russian,cortar,Spanish,snijden,Dutch,skära,Swedish,kṛntáti,Sanskrit
116 | split,English,fendre,French,spalten,German,dividere,Italian,scindendae,Latin,raskalyvat',Russian,dividido,Spanish,splijten,Dutch,dela,Swedish,bhinátti,Sanskrit
117 | stab,English,poignarder,French,stechen,German,pugnalata,Italian,confodisti,Latin,votknut',Russian,puñalada,Spanish,steken,Dutch,sticka,Swedish,vídhyati,Sanskrit
118 | scratch,English,gratter,French,kratzen,German,graffiare,Italian,scalpere,Latin,carapat',Russian,rayar,Spanish,krabben,Dutch,klia,Swedish,likháti,Sanskrit
119 | dig,English,creuser,French,graben,German,scavare,Italian,fodere,Latin,kopat',Russian,cavar,Spanish,graven,Dutch,gräva,Swedish,khánati,Sanskrit
120 | swim,English,nager,French,schwimmen,German,nuotare,Italian,natare,Latin,plavat',Russian,nadar,Spanish,zwemmen,Dutch,simma,Swedish,plávate,Sanskrit
121 | fly,English,voler,French,fliegen,German,volare,Italian,volare,Latin,letat',Russian,volar,Spanish,vliegen,Dutch,flyga,Swedish,pátati,Sanskrit
122 | walk,English,marcher,French,gehen,German,camminare,Italian,ambulare,Latin,hodit',Russian,caminar,Spanish,lopen,Dutch,gå,Swedish,éti,Sanskrit
123 | come,English,venir,French,kommen,German,venire,Italian,venire,Latin,prihodit',Russian,venir,Spanish,komen,Dutch,komma,Swedish,ā́gacchati,Sanskrit
124 | lie,English,s'étendre,French,liegen,German,mentire,Italian,mentiri,Latin,lezhat',Russian,mentir,Spanish,liggen,Dutch,ligga,Swedish,śéte,Sanskrit
125 | sit,English,s'asseoir,French,sitzen,German,sedersi,Italian,sedere,Latin,sidet',Russian,sentarse,Spanish,zitten,Dutch,sitta,Swedish,sī́dati,Sanskrit
126 | stand,English,se lever,French,stehen,German,stare in piedi,Italian,stare,Latin,stojat',Russian,estar de pie,Spanish,staan,Dutch,stå,Swedish,tíṣṭhati,Sanskrit
127 | turn,English,tourner,French,drehen,German,girare,Italian,converte,Latin,povorachivat',Russian,vuelta,Spanish,draaien,Dutch,svänga,Swedish,vártate,Sanskrit
128 | fall,English,tomber,French,fallen,German,caduta,Italian,cadere,Latin,padat',Russian,caer,Spanish,vallen,Dutch,falla,Swedish,pádyate,Sanskrit
129 | give,English,donner,French,geben,German,dare,Italian,dare,Latin,davat',Russian,dar,Spanish,geven,Dutch,ge,Swedish,dádāti,Sanskrit
130 | hold,English,tenir,French,halten,German,tenere,Italian,tenere,Latin,derzhat',Russian,mantener,Spanish,houden,Dutch,hålla,Swedish,dhárati,Sanskrit
131 | squeeze,English,serrer,French,quetschen,German,spremere,Italian,exprimito,Latin,szhimat',Russian,apretar,Spanish,knijpen,Dutch,klämma,Swedish,mṛdnā́ti,Sanskrit
132 | rub,English,frotter,French,reiben,German,strofinare,Italian,fricare,Latin,teret',Russian,frotar,Spanish,wrijven,Dutch,gnida,Swedish,ghárṣati,Sanskrit
133 | wash,English,laver,French,waschen,German,lavare,Italian,lavabit,Latin,myt',Russian,lavar,Spanish,wassen,Dutch,tvätta,Swedish,kṣā́layati,Sanskrit
134 | wipe,English,essuyer,French,wischen,German,pulire,Italian,extergunt,Latin,vytirat',Russian,limpiar,Spanish,vegen,Dutch,rensa,Swedish,mā́rṣṭi,Sanskrit
135 | pull,English,tirer,French,ziehen,German,tirare,Italian,attrahendam,Latin,tjanut',Russian,tirar,Spanish,trekken,Dutch,dra,Swedish,kárṣati,Sanskrit
136 | push,English,pousser,French,drücken,German,spingere,Italian,ventilabis,Latin,tolkat',Russian,empuje,Spanish,duwen,Dutch,trycka,Swedish,nudáti,Sanskrit
137 | throw,English,jeter,French,werfen,German,buttare,Italian,subvertat,Latin,brosat',Russian,lanzar,Spanish,werpen,Dutch,kasta,Swedish,kṣipáti,Sanskrit
138 | tie,English,lier,French,binden,German,legare,Italian,iungite,Latin,svjazyvat',Russian,atar,Spanish,knopen,Dutch,knyta,Swedish,badhnā́ti,Sanskrit
139 | sew,English,coudre,French,nähen,German,cucire,Italian,consuendi,Latin,shit',Russian,coser,Spanish,naaien,Dutch,sy,Swedish,sī́vyati,Sanskrit
140 | count,English,compter,French,zählen,German,contare,Italian,numerabit,Latin,schitat',Russian,contar,Spanish,tellen,Dutch,räkna,Swedish,gáṇayati,Sanskrit
141 | say,English,dire,French,sagen,German,dire,Italian,dicere,Latin,skazat',Russian,decir,Spanish,zeggen,Dutch,säga,Swedish,vákti,Sanskrit
142 | sing,English,chanter,French,singen,German,cantare,Italian,cantare,Latin,pet',Russian,cantar,Spanish,zingen,Dutch,sjunga,Swedish,gā́yati,Sanskrit
143 | play,English,jouer,French,spielen,German,giocare,Italian,ludere,Latin,igrat',Russian,jugar,Spanish,spelen,Dutch,leka,Swedish,dī́vyati,Sanskrit
144 | float,English,flotter,French,schweben,German,galleggiante,Italian,supernatet,Latin,plavat',Russian,flotar,Spanish,zweven,Dutch,flyta,Swedish,plávate,Sanskrit
145 | flow,English,couler,French,fließen,German,flusso,Italian,fluere,Latin,tech',Russian,flujo,Spanish,vloeien,Dutch,rinna,Swedish,sárati,Sanskrit
146 | freeze,English,geler,French,frieren,German,congelare,Italian,duratus,Latin,zamerzat',Russian,congelar,Spanish,vriezen,Dutch,frysa,Swedish,śī́yate,Sanskrit
147 | swell,English,gonfler,French,schwellen,German,gonfiarsi,Italian,intumescunt,Latin,puhnut',Russian,hincharse,Spanish,zwellen,Dutch,svälla,Swedish,śváyati,Sanskrit
148 | sun,English,soleil,French,sonne,German,sole,Italian,sol,Latin,solnce,Russian,sol,Spanish,zon,Dutch,sol,Swedish,sū́rya,Sanskrit
149 | moon,English,lune,French,mond,German,luna,Italian,luna,Latin,luna,Russian,luna,Spanish,maan,Dutch,måne,Swedish,mā́sa,Sanskrit
150 | star,English,étoile,French,stern,German,stella,Italian,stella,Latin,zvezda,Russian,estrella,Spanish,ster,Dutch,stjärna,Swedish,nákṣatra,Sanskrit
151 | water,English,eau,French,wasser,German,acqua,Italian,aqua,Latin,voda,Russian,agua,Spanish,water,Dutch,vatten,Swedish,jalá,Sanskrit
152 | rain,English,pluie,French,regen,German,pioggia,Italian,pluvia,Latin,dozhd',Russian,lluvia,Spanish,regen,Dutch,regn,Swedish,varṣá,Sanskrit
153 | river,English,rivière,French,fluß,German,fiume,Italian,flumen,Latin,reka,Russian,río,Spanish,rivier,Dutch,flod,Swedish,nadī́,Sanskrit
154 | lake,English,lac,French,see,German,lago,Italian,lacus,Latin,ozero,Russian,lago,Spanish,meer,Dutch,sjö,Swedish,sáras,Sanskrit
155 | sea,English,mer,French,meer,German,mare,Italian,mare,Latin,more,Russian,mar,Spanish,zee,Dutch,hav,Swedish,samudrá,Sanskrit
156 | salt,English,sel,French,salz,German,sale,Italian,sal,Latin,sol',Russian,sal,Spanish,zout,Dutch,salt,Swedish,lavaṇá,Sanskrit
157 | stone,English,pierre,French,stein,German,pietra,Italian,lapis,Latin,kamen',Russian,piedra,Spanish,steen,Dutch,sten,Swedish,áśman,Sanskrit
158 | sand,English,sable,French,sand,German,sabbia,Italian,harena,Latin,pesok,Russian,arena,Spanish,zand,Dutch,sand,Swedish,pāṃsú,Sanskrit
159 | dust,English,poussière,French,staub,German,polvere,Italian,pulvis,Latin,pyl',Russian,polvo,Spanish,stof,Dutch,damm,Swedish,reṇú,Sanskrit
160 | earth,English,terre,French,erde,German,terra,Italian,terra,Latin,zemlja,Russian,tierra,Spanish,aarde,Dutch,jord,Swedish,kṣám,Sanskrit
161 | cloud,English,nuage,French,wolke,German,nuvola,Italian,nubes,Latin,oblako,Russian,nube,Spanish,wolk,Dutch,moln,Swedish,nábhas,Sanskrit
162 | fog,English,brouillard,French,nebel,German,nebbia,Italian,caligo,Latin,tuman,Russian,niebla,Spanish,mist,Dutch,dimma,Swedish,míh,Sanskrit
163 | sky,English,ciel,French,himmel,German,cielo,Italian,caelum,Latin,nebo,Russian,cielo,Spanish,lucht,Dutch,himmel,Swedish,ākāśá,Sanskrit
164 | wind,English,vent,French,wind,German,vento,Italian,ventus,Latin,veter,Russian,viento,Spanish,wind,Dutch,vind,Swedish,vāyú,Sanskrit
165 | snow,English,neige,French,schnee,German,neve,Italian,nix,Latin,sneg,Russian,nieve,Spanish,sneeuw,Dutch,snö,Swedish,himá,Sanskrit
166 | ice,English,glace,French,eis,German,ghiaccio,Italian,glacies,Latin,ljod,Russian,hielo,Spanish,ijs,Dutch,is,Swedish,himá,Sanskrit
167 | smoke,English,fumée,French,rauch,German,fumare,Italian,fumigant,Latin,dym,Russian,fumar,Spanish,rook,Dutch,rök,Swedish,dhūmá,Sanskrit
168 | fire,English,feu,French,feuer,German,fuoco,Italian,ignis,Latin,ogon',Russian,fuego,Spanish,vuur,Dutch,eld,Swedish,agní,Sanskrit
169 | ashes,English,cendres,French,asche,German,ceneri,Italian,cinere,Latin,zola,Russian,cenizas,Spanish,as,Dutch,aska,Swedish,ā́sa,Sanskrit
170 | burn,English,brûler,French,brennen,German,bruciare,Italian,adolebit,Latin,goret',Russian,quemar,Spanish,branden,Dutch,brinna,Swedish,dahati,Sanskrit
171 | road,English,route,French,straße,German,strada,Italian,via,Latin,doroga,Russian,carretera,Spanish,weg,Dutch,väg,Swedish,patha,Sanskrit
172 | mountain,English,montagne,French,berg,German,montagna,Italian,mons,Latin,gora,Russian,montaña,Spanish,berg,Dutch,berg,Swedish,girí,Sanskrit
173 | red,English,rouge,French,rot,German,rosso,Italian,ruber,Latin,krasnyj,Russian,rojo,Spanish,rood,Dutch,röd,Swedish,rakta,Sanskrit
174 | green,English,vert,French,grün,German,verde,Italian,viridi,Latin,zeljonyj,Russian,verde,Spanish,groen,Dutch,grön,Swedish,harít,Sanskrit
175 | yellow,English,jaune,French,gelb,German,giallo,Italian,flavis,Latin,zhjoltyj,Russian,amarillo,Spanish,geel,Dutch,gul,Swedish,pīta,Sanskrit
176 | white,English,blanc,French,weiß,German,bianco,Italian,albus,Latin,belyj,Russian,blanco,Spanish,wit,Dutch,vit,Swedish,śvetá,Sanskrit
177 | black,English,noir,French,schwarz,German,nero,Italian,niger,Latin,chjornyj,Russian,negro,Spanish,zwart,Dutch,svart,Swedish,kṛṣṇá,Sanskrit
178 | night,English,nuit,French,nacht,German,notte,Italian,nocte,Latin,noch',Russian,noche,Spanish,nacht,Dutch,natt,Swedish,rā́tri,Sanskrit
179 | day,English,jour,French,tag,German,giorno,Italian,die,Latin,den',Russian,día,Spanish,dag,Dutch,dag,Swedish,diná,Sanskrit
180 | year,English,an,French,jahr,German,anno,Italian,anno,Latin,god,Russian,año,Spanish,jaar,Dutch,år,Swedish,varṣá,Sanskrit
181 | warm,English,chaud,French,warm,German,caldo,Italian,tepido,Latin,tjoplyj,Russian,caliente,Spanish,warm,Dutch,varm,Swedish,taptá,Sanskrit
182 | cold,English,froid,French,kalt,German,freddo,Italian,frigus,Latin,holodnyj,Russian,frío,Spanish,koud,Dutch,kall,Swedish,śītá,Sanskrit
183 | full,English,plein,French,voll,German,completo,Italian,plenus,Latin,polnyj,Russian,completo,Spanish,vol,Dutch,full,Swedish,pūrṇá,Sanskrit
184 | new,English,nouveau,French,neu,German,nuovo,Italian,novus,Latin,novyj,Russian,nuevo,Spanish,nieuw,Dutch,ny,Swedish,náva,Sanskrit
185 | old,English,vieux,French,alt,German,vecchio,Italian,vetus,Latin,staryj,Russian,viejo,Spanish,oud,Dutch,gammal,Swedish,jīrṇá,Sanskrit
186 | good,English,bon,French,gut,German,buono,Italian,bonum,Latin,horoshij,Russian,bueno,Spanish,goed,Dutch,bra,Swedish,vásu,Sanskrit
187 | bad,English,mauvais,French,schlecht,German,male,Italian,malus,Latin,plohoj,Russian,malo,Spanish,slecht,Dutch,dålig,Swedish,pāpá,Sanskrit
188 | rotten,English,pourri,French,verrottet,German,marcio,Italian,putrida,Latin,gniloj,Russian,podrido,Spanish,rot,Dutch,rutten,Swedish,pū́ti,Sanskrit
189 | dirty,English,sale,French,schmutzig,German,sporco,Italian,sordida,Latin,grjaznyj,Russian,sucio,Spanish,vies,Dutch,smutsig,Swedish,malina,Sanskrit
190 | straight,English,droit,French,gerade,German,dritto,Italian,recta,Latin,prjamoj,Russian,recto,Spanish,recht,Dutch,rak,Swedish,ṛjú,Sanskrit
191 | round,English,rond,French,rund,German,intorno,Italian,circum,Latin,kruglyj,Russian,redondear,Spanish,rond,Dutch,rund,Swedish,vṛttá,Sanskrit
192 | sharp,English,tranchant,French,scharf,German,affilato,Italian,acutus,Latin,ostryj,Russian,agudo,Spanish,scherp,Dutch,vass,Swedish,tīkṣṇá,Sanskrit
193 | dull,English,émoussé,French,stumpf,German,opaco,Italian,hebetes,Latin,tupoj,Russian,aburrido,Spanish,stomp,Dutch,slö,Swedish,kuṇṭha,Sanskrit
194 | smooth,English,lisse,French,glatt,German,lisciare,Italian,aequabis,Latin,gladkij,Russian,suavizar,Spanish,glad,Dutch,len,Swedish,ślakṣṇá,Sanskrit
195 | wet,English,mouillé,French,nass,German,bagnato,Italian,udo,Latin,mokryj,Russian,húmedo,Spanish,nat,Dutch,våt,Swedish,ārdrá,Sanskrit
196 | dry,English,sec,French,trocken,German,asciugare,Italian,siccabis,Latin,suhoj,Russian,secar,Spanish,droog,Dutch,torr,Swedish,śúṣka,Sanskrit
197 | correct,English,juste,French,richtig,German,correggere,Italian,corrigere,Latin,pravil'nyj,Russian,corregir,Spanish,juist,Dutch,rätt,Swedish,śuddhá,Sanskrit
198 | near,English,proche,French,nah,German,vicino,Italian,prope,Latin,blizkij,Russian,cerca,Spanish,dichtbij,Dutch,nära,Swedish,neda,Sanskrit
199 | far,English,loin,French,weit,German,lontano,Italian,longe,Latin,daljokij,Russian,lejos,Spanish,ver,Dutch,långt bort,Swedish,dūrá,Sanskrit
200 | right,English,à droite,French,rechts,German,destra,Italian,ius,Latin,pravyj,Russian,derecho,Spanish,rechts,Dutch,höger,Swedish,dákṣiṇa,Sanskrit
201 | left,English,à gauche,French,links,German,a sinistra,Italian,reliquit,Latin,levyj,Russian,izquierda,Spanish,links,Dutch,vänster,Swedish,savyá,Sanskrit
202 | at,English,à,French,bei,German,a,Italian,ad,Latin,pri,Russian,en,Spanish,aan,Dutch,hos,Swedish,,Sanskrit
203 | in,English,dans,French,in,German,in,Italian,in,Latin,v,Russian,en,Spanish,in,Dutch,i,Swedish,,Sanskrit
204 | with,English,avec,French,mit,German,con,Italian,cum,Latin,s,Russian,con,Spanish,met,Dutch,med,Swedish,sahá,Sanskrit
205 | and,English,et,French,und,German,e,Italian,et,Latin,i,Russian,y,Spanish,en,Dutch,och,Swedish,ca,Sanskrit
206 | if,English,si,French,wenn,German,se,Italian,si,Latin,esli,Russian,si,Spanish,als,Dutch,om,Swedish,yádi,Sanskrit
207 | because,English,parce que,French,weil,German,perché,Italian,quia,Latin,potomu chto,Russian,porque,Spanish,omdat,Dutch,eftersom,Swedish,hí,Sanskrit
208 | name,English,nom,French,name,German,nome,Italian,nomen,Latin,imja,Russian,nombre,Spanish,naam,Dutch,namn,Swedish,nā́man,Sanskrit
209 | 


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/README.md:
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1 | # Natural-Language-Processing-with-Python-Analyzing-Text-with-the-Natural-Language-Toolkit
2 | 
3 | My solutions to selected exercises to "Natural Language Processing with Python
4 | – Analyzing Text with the Natural Language Toolkit" by Steven Bird, Ewan Klein, and Edward Loper.  Unless noted otherwise, all solutions are my own and represent original material.  The HTML version of the NLTK book is available at https://www.nltk.org/book/.
5 | 


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