├── .gitignore ├── README.md ├── assignments.html ├── cs224n.css ├── fp-presentation.shtml ├── grading.shtml ├── handouts ├── 224NProjectInfo.pdf ├── CGW_instructions.pdf ├── CS224N_DeepNLP_Week7_lecture1-6up.pdf ├── CS224N_DeepNLP_Week7_lecture1.pdf ├── CS224N_DeepNLP_Week7_lecture2-6up.pdf ├── CS224N_DeepNLP_Week7_lecture2.pdf ├── CS224N_DeepNLP_Week7_lecture3-6up.pdf ├── CS224N_DeepNLP_Week7_lecture3.pdf ├── Collins_annotated.pdf ├── Computational Semantics 2-6up.pdf ├── Computational Semantics 2.pdf ├── Computational Semantics-6up.pdf ├── Computational Semantics.pdf ├── Computational-Semantics-2-6up.pdf ├── Computational-Semantics-2.pdf ├── Computational-Semantics-6up.pdf ├── Computational-Semantics.pdf ├── ExamplePresentation.pdf ├── Knight_workbook_with_notes.pdf ├── MaxentTutorial-16x9-6up.pdf ├── MaxentTutorial-16x9-FeatureClassifiers-6up.pdf ├── MaxentTutorial-16x9-FeatureClassifiers.pdf ├── MaxentTutorial-16x9-MEMMs-Smoothing-6up.pdf ├── MaxentTutorial-16x9-MEMMs-Smoothing.pdf ├── MaxentTutorial-16x9-Smoothing-6up.pdf ├── MaxentTutorial-16x9-Smoothing.pdf ├── MaxentTutorial-16x9.pdf ├── SLoSP-2013-1-6up.pdf ├── SLoSP-2013-1.pdf ├── SLoSP-2013-4-dependencies-6up.pdf ├── SLoSP-2013-4-dependencies.pdf ├── SLoSP-2014-4-dependencies-6up.pdf ├── SLoSP-2014-4-dependencies.pdf ├── afz-tutorial.acl.2013-learning.pdf ├── cgw │ ├── cgw-instructions.pdf │ └── cgw-introduction.pdf ├── cl-semantics-slides-2012.pdf ├── cl-semantics-slides-2015.pdf ├── cs224n-QA-2013.pdf ├── cs224n-lecture-computational-semantics.pdf ├── cs224n-lecture-topic-models.pdf ├── cs224n-lecture1-1-24.pdf ├── cs224n-lecture1-6up-1-24.pdf ├── cs224n-lecture1-6up.pdf ├── cs224n-lecture1.pdf ├── cs224n-lecture10-coreference-6up.pdf ├── cs224n-lecture10-coreference.pdf ├── cs224n-lecture11-coreference-6up.pdf ├── cs224n-lecture11-coreference.pdf ├── cs224n-lecture16-nmt-6up.pdf ├── cs224n-lecture16-nmt.pdf ├── cs224n-lecture2-WordAlign-6up.pdf ├── cs224n-lecture2-WordAlign.pdf ├── cs224n-lecture3-MT-6up.pdf ├── cs224n-lecture3-MT.pdf ├── cs224n-lecture4-PhraseBasedMT-6up.pdf ├── cs224n-lecture4-PhraseBasedMT.pdf ├── cs224n-lecture5-language-models-6up.pdf ├── cs224n-lecture5-language-models.pdf ├── cs224nprojectideas.pdf ├── final-project-guidelines.pdf ├── final-project-ideas.pdf ├── is1-cgw.pptx └── pa4-2.pdf ├── header.html ├── img ├── backstripe.gif ├── blue-10.gif ├── gates-200.jpg ├── green-10.gif ├── i_could_care_less.png ├── new2.gif ├── nlp-logo-navbar.jpg ├── question_mark.jpg ├── spacer.gif └── su_seal.gif ├── index.shtml ├── participation.shtml ├── scripts └── md5.js ├── selfcheck.shtml ├── syllabus.shtml └── textbook.shtml /.gitignore: -------------------------------------------------------------------------------- 1 | WWW_OLD/ 2 | .DS_Store 3 | 4 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # cs224n-web 2 | http://cs224n.stanford.edu 3 | -------------------------------------------------------------------------------- /assignments.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | CS 224N / Ling 284 Assignments 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 20 | 21 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 367 |
80 | 81 |

82 | This page describes assignments and grading policies for CS224N / Ling 284: 83 |

98 |

99 | 100 | 101 | 102 |
103 |

Grading

104 | 105 |

106 | Course grades will be based 60% on the three programming assignments (20% each), 34% on the final project, and 6% on the quizzes (0.33% for 109 | each). 110 |

111 | 112 | 113 | 114 | 115 | 116 |
117 |

Quizzes

118 | 119 |

120 | During each class, a question will be asked in class, related to the 121 | topics covered that day. The response to each question determines 122 | 0.33% of your final grade, totaling to 6% over all 18 lectures 123 | scheduled for this quarter. Use the quiz submission script at Quiz Submissions.

124 | 125 |

126 | The responses for any week's classes must reach the course staff by 127 | 5pm on the following Sunday. (E.g., responses to the questions asked 128 | in class on Monday 1/10/11 and/or Wednesday 1/12/11 must reach the 129 | course staff by 5pm on Sunday 1/16/11).

130 | 131 |
132 |

Programming Assignments

133 | 134 |

135 | There will be three substantial programming assignments, each 136 | exploring a core NLP task. 137 |

138 | 139 |
140 | 141 |
142 | 143 | PA1: N-Gram Language Models 144 | 145 |
146 |
147 | We'll build a language model based on n-gram statistics 148 | estimated from a large corpus, and test our model's ability to 149 | help with a speech recognition task. (Due Wednesday 1/19/11.) 150 |
151 | 152 |
153 | 154 | PA2: Word Alignment Models for Machine Translation 155 | 156 |
157 |
158 | We'll build word alignment models based on IBM models 1 and 2. It 159 | will be trained and tested on the Hansard corpus, consisting of 160 | parallel English and French sentences. Paste in your language 161 | model from PA1, and with the provided Greedy Decoder, you have a 162 | complete statistical machine translation system, to try out on the 163 | provided French, German, and Spanish corpora. (Due Wednesday 164 | 2/2/11.) 165 |
166 | 167 |
168 | 169 | PA3: Maximum Entropy Markov Models & Treebank Parsing 170 | 171 |
172 |
173 | This assignment looks at named entity recognition and parsing. The 174 | aim is to examine whether pre-chunking of named entities can 175 | improve the performance of a statistical parser trained on 176 | financial newswire text when applied to the task of parsing 177 | biomedical research articles. You will build a maximum entropy 178 | classifier, which will be incorporated into a maximum entropy 179 | Markov model for doing named entity recognition on biomedical 180 | text. You will also implement the parsing algorithm for a broad 181 | coverage statistical treebank parser. We have included in the 182 | support code the ability to chunk entities into a single word, and 183 | then to pass this chunked sentence to the parser, so that you can 184 | then informally compare the performance of the parser on chunked 185 | and unchunked input. (Due Wednesday 2/16/11 - deadline extended to 11:59pm PST.) 186 |
187 | 188 |
189 | 190 | 191 | 192 | 193 |
194 |

Final Project

195 | 196 |

197 | In addition, there will be a final programming project on a topic of 198 | your own choosing. See the final 200 | project guide for more information. 201 |

202 | 203 |

204 | A short, ungraded project proposal will be due on Wednesday 2/9/11. 205 | Final project write-ups will be due on Wednesday 3/9/11. Students will 206 | give short project presentations on Thursday 3/17/11, from 12:15pm to 207 | 3:15pm. 208 |

209 | 210 |

211 | You may find it helpful to look at final projects from 213 | previous years. 214 |

215 | 216 | 217 | 218 | 219 |
220 |

Collaboration Policy

221 | 222 |

223 | For both the programming assignments and final project, you're free 224 | to work alone, but you're also allowed (and indeed encouraged) to 225 | work in teams. This means developing ideas together, writing code 226 | together, and submitting a joint report. 227 |

228 | 229 |
    230 |
  • 231 | For the programming assignments, only two-person 232 | teams are allowed. 233 |
  • 234 |
  • 235 | For the final project, teams of up to three 236 | people are allowed. 237 |
  • 238 |
239 | 240 |

241 | However, if you collaborate, your submission must include a 242 | statement describing the contributions of each collaborator. 243 | For example, "We did the entire project as pair programming over 244 | several late nights in our dorm rooms". Or, "Sue built the initial 245 | parser, while Joe worked on improving parse quality through the use 246 | of features and on improving runtime speed by profiling." 247 |

248 | 249 |

250 | Ordinarily, all team members will receive the same grade for an 251 | assignment—though we reserve the right, in case of egregiously 252 | unequal contributions, to assign different grades to different team 253 | members. 254 |

255 | 256 |

257 | Please ask if you have any questions about the collaboration 258 | policy, and make sure you adhere to it. 259 |

260 | 261 | 262 | 263 | 264 |
265 |

Electronic Submission

266 | 267 |

268 | For each programming assignment and for the final project, you'll 269 | submit your program using a Unix script that we've prepared. 270 | To submit your program, first put all the files to be submitted in 271 | one directory on a Leland machine (or any machine from which you 272 | can access the Leland AFS filesystem). This should include all 273 | source code files, but should not include compiled class files or 274 | large data files. Normally, your submission directory will have a 275 | subdirectory named src which contains all your source 276 | code. When you're ready to submit, type: 277 |

278 | 279 |
280 | /afs/ir/class/cs224n/bin/submit-pa1 281 |
282 | 283 |

284 | (Or submit-pa2, or submit-pa2, or 285 | submit-fp, as appropriate.) This will (recursively) copy 286 | everything in your submission directory into the official 287 | submission directory for the class. If you need to resubmit it 288 | type 289 |

290 | 291 |
292 | /afs/ir/class/cs224n/bin/submit-pa1 -replace 293 |
294 | 295 |

296 | We will compile and run your program on the Leland systems, using 297 | ant and our standard build.xml to compile, and 298 | using java to run. So, please make sure your program 299 | compiles and runs without difficulty on the Leland machines. If 300 | there's anything special we need to know about compiling or running 301 | your program, please include a README file with your 302 | submission. Your code doesn't have to be beautiful but we should 303 | be able to scan it and figure out what you did without too much 304 | pain. 305 |

306 | 307 | 308 | 309 |
310 |

Late Day Policy

311 | 312 |

313 | All assignments are due at 5pm on the assigned due date. A grading 314 | penalty will be applied to late assignments. We recognize that 315 | students may face unusual circumstances and require some 316 | flexibility in the course of the quarter; therefore each student 317 | will be granted a total of 318 | five free late (calendar) days to use as he or she sees 319 | fit. Once these late days are exhausted, any assignment turned in 320 | late will be penalized 10% per late day. Each 24 hours or part 321 | thereof that a assignment is late uses up one full late day. 322 |

323 | 324 |

325 | Late days may be used for the final project report; however, no 326 | final project reports will be accepted after 327 | Monday 3/14/11. 328 |

329 | 330 |

331 | To hand in the report late, there is a hand-in box in the basement of Gates, near the bottom of the A-wing stairwell. 332 | You can find directions to it here. To get into the basement after the building is locked, 333 | slide your SUID card in the card reader by the main basement entrance. For code submitted late, 334 | please write the date and time of submission on your report and sign it before placing it in the box. 335 | 336 |

It is an honor code violation to write down the wrong time. 337 |

338 | 339 |

340 | When students collaborate on an assignment, and the assignment is 341 | submitted late, late days are deducted from each team member's 342 | balance. Altruistic team members are allowed to "donate" late days 343 | to a collaborator if they wish. 344 |

345 | 346 | 347 | 348 |
349 |

Regrading requests

350 | 351 |

352 | If you feel you deserved a better grade on an assignment, you may 353 | submit a regrade request in writing to the TA responsible. 354 | Your request should briefly summarize why you feel the original 355 | grade was unfair. Your TA will take a day or two to reevaluate 356 | your assignment, and then issue a decision. If you're still not 357 | satisfied, you can then appeal (again, in writing) to 358 | the course instructors, Bill MacCartney and Prof. Gerald Penn. 359 |

360 | 361 |

362 | Note that in regrading an assignment, we may reevaluate any part 363 | of it, not just the part you bring to our attention. 364 |

365 | 366 |
368 |
369 | 370 | 371 | 372 | 373 | 374 | 375 | 376 | 377 | 378 |
379 | 380 | 381 | 382 | 384 | 389 | 390 | 391 | 392 | 393 | 394 | 395 | -------------------------------------------------------------------------------- /cs224n.css: -------------------------------------------------------------------------------- 1 | body { 2 | background-color: white; 3 | background-image: url(img/backstripe.gif); 4 | background-repeat: repeat-y; 5 | margin-left: 30px; 6 | margin-right: 0px; 7 | margin-top: 0px; 8 | font-family: Lucida Sans, Arial, Helvetica, sans-serif; 9 | font-size: 12px; 10 | line-height: 16px; 11 | } 12 | p { 13 | font-family: Lucida Sans, Arial, Helvetica, sans-serif; 14 | font-size: 12px; 15 | line-height: 16px; 16 | } 17 | .small { 18 | font-size: 10px; 19 | line-height: 12px; 20 | } 21 | .p { 22 | font-family: Lucida Sans, Arial, Helvetica, sans-serif; 23 | font-size: 12px; 24 | line-height: 16px; 25 | } 26 | .review { 27 | color: green; 28 | } 29 | .border { 30 | border: 1px solid #990000; 31 | } 32 | .bottomborder { 33 | border-bottom: 1px solid #990000; 34 | } 35 | .indent { 36 | margin-left: 3em; 37 | } 38 | table { 39 | font-family: Lucida Sans, Arial, Helvetica, sans-serif; 40 | font-size: 12px; 41 | line-height: 16px; 42 | } 43 | th { 44 | font-weight: bold; 45 | color: #990000; 46 | } 47 | ul { 48 | font-size: 12px; 49 | line-height: 16px; 50 | } 51 | ul.nobullet { 52 | list-style-type: none; 53 | } 54 | .spaced { 55 | margin-top: 12px; 56 | } 57 | li.standalone { 58 | font-size: 12px; 59 | line-height: 16px; 60 | list-style-position: outside; 61 | } 62 | dl { 63 | font-family: Lucida Sans, Arial, Helvetica, sans-serif; 64 | font-size: 12px; 65 | line-height: 16px; 66 | } 67 | dl.indent { 68 | margin-left: 3em; 69 | } 70 | h1 { 71 | font-size: 18px; 72 | font-weight: bold; 73 | color: #990000; 74 | padding-top: 5px; 75 | border-top: solid; 76 | border-top-width: 1px; 77 | } 78 | h2 { 79 | font-size: 14px; 80 | font-weight: bold; 81 | color: #990000; 82 | margin-top: 14px; 83 | } 84 | h3 { 85 | font-size: 12px; 86 | font-weight: bold; 87 | color: #990000; 88 | margin-top: 20px; 89 | margin-bottom: 0em; 90 | } 91 | p.title { 92 | font-family: Palatino, Georgia, Book Antiqua, serif; 93 | font-size: 22px; 94 | line-height: 24px; 95 | } 96 | table.navbar { 97 | background-color: #F5EEC6; 98 | margin-left: -9px; 99 | } 100 | .crimson { color: #990000; } 101 | .gray { color: gray; } 102 | .green { color: green; } 103 | p.navbar { 104 | font-size: 12px; 105 | font-weight: bold; 106 | color: #990000; 107 | } 108 | table.footer { 109 | margin-top: 10px; 110 | margin-left: -9px; 111 | border-top: solid; 112 | border-top-width: 1px; 113 | } 114 | a:link { 115 | text-decoration: none; 116 | color: blue; 117 | } 118 | a:visited { 119 | text-decoration: none; 120 | color: #660099; 121 | } 122 | a:hover { 123 | text-decoration: underline; 124 | } 125 | a[name]:hover { 126 | text-decoration: inherit; 127 | color: inherit; 128 | background: inherit 129 | } 130 | a:active { 131 | text-decoration: none; 132 | } 133 | 134 | a.navbar:link { 135 | text-decoration: none; 136 | color: #990000; 137 | background: #F5EEC6; 138 | } 139 | a.navbar:visited { 140 | text-decoration: none; 141 | color: #990000; 142 | background: #F5EEC6; 143 | } 144 | a.navbar:hover { 145 | text-decoration: underline; 146 | } 147 | a.navbar:active { 148 | text-decoration: none; 149 | } 150 | a { 151 | color: #003333; 152 | } 153 | .over { 154 | color: #993300; 155 | } 156 | .faqdate { 157 | font-size: 10px; 158 | line-height: 12px; 159 | color: gray; 160 | } 161 | -------------------------------------------------------------------------------- /fp-presentation.shtml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 |
5 |

Final Project Presentations

6 | 7 |

Lecture 19 (12/1/15, Tuesday)

8 |
    9 |
  1. lukedeo / alainez: Sequential CNNs for Multi-Sentence Text Classification
  2. 10 |
  3. yuyan1: Tracking disease progression in radiology reports
  4. 11 |
  5. cwind / xiaoshiw / danyangw: Machine Comprehension using Feature Engineering
  6. 12 |
  7. sjtang / hanjiang: Pruning Deep Recurrent Neural Networks
  8. 13 |
  9. yetian1 / jiyue: Let Computers Do Reading Comprehension
  10. 14 |
  11. nihit / truongk / rgupta9: Predicting success on Kickstarter
  12. 15 |
  13. ekyauk / aacharya / emjtang: Acoustic Cues in Bilingual Speakers
  14. 16 |
  15. arastogi: Context Encoding LSTM
  16. 17 |
  17. justinkk: Video games for annotating NLI data
  18. 18 |
  19. dmg1 / bstate: The Language of Experts
  20. 19 | 20 |
  21. justinfu / dthirman: Medical Record Understanding
  22. 21 |
  23. yilunw: Understanding Personality through Social Media
  24. 22 |
  25. mrpeters / ulmerb / matthew0: Literary Social Network Analysis
  26. 23 |
  27. asax / dmoore2: Adversarial Examples for NLP
  28. 24 |
  29. danae: Measuring the Web's Dark Matter
  30. 25 | 26 |
  31. sjtodd: Measuring Functional Load with Word Vectors
  32. 27 |
28 | 29 |

Lecture 20 (12/3/15, Thursday)

30 |
    31 |
  1. onkursen / icaswell / anie: Applying Adversarial Examples to Neural Language Modeling
  2. 32 |
  3. abisee: Exploiting Redundancy in Neural Machine Translation
  4. 33 |
  5. naveen67 / rshu15: Annotation for Word Sense Disambiguation
  6. 34 |
  7. viswa / sameepb: Editing Behavior on Kickstarter
  8. 35 |
  9. tstand: Neural Word Sense Disambiguation
  10. 36 | 37 |
  11. klopyrev: Generating News Headlines with RNNs
  12. 38 |
  13. telin / dahuang: AI-Complete Question Answering After Automated Comprehendly Reading
  14. 39 |
  15. agong / jenylu: Picking out Good Dishes from Yelp
  16. 40 |
  17. ajchin / epatters: Extracting Family Trees from Literary Texts
  18. 41 |
  19. jaycaz / martinam / cdixit: Large Scale Language Classification
  20. 42 |
  21. qiaojing / wyixin: Machine Comprehension using Syntactic Features
  22. 43 |
  23. lmurata: An Attempt to Beat the Turing Test
  24. 44 |
  25. vishesh: Alignment Trees
  26. 45 |
  27. aalifimoff / jli14: Abstractive Summarisation with Neural Networks
  28. 46 |
47 | 48 | 49 | -------------------------------------------------------------------------------- /grading.shtml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 |
6 |

Grading

7 |

There will be four substantial programming assignments, each exploring a core NLP task. They are a chance to see real, close to state-of-the-art tools and techniques in action, and where students learn a lot of the material of the class. You have the choice to do the fourth programming assignment, or to do a final programming project on a topic of your own choosing.

8 |

There will also be short online quizzes on the edX website, which aim to get you to think about material you read/hear which isn't directly in a programming assignment.

9 | 10 |

Finally, you will be rewarded for participating in the 11 | Competitive Grammar Writing exercise and for general class participation.

12 |

In summary, the final grade will be computed as follows: 13 |

36 |

37 |

General Class Participation Guidelines

38 |

This includes but is not limited to Piazza participation, coming to and being active in class, talking to TAs or the professor in programming sessions or office hours, and mid-quarter survey completion.

39 | 40 |

We will be taking attendance a couple of times in the second half of the quarter to make sure we know who regularly comes to class, even if you are quiet. Even Piazza participation alone can fetch you the full 3% for general class participation. However we encourage you to attend the lectures and ask questions during the class as a surefire way of earning the participation points.

41 | 42 |

Collaboration Policy

43 |

For both the programming assignments and final project, you're free to work alone, but you're also allowed (and indeed encouraged) to work in teams. This means developing ideas together, writing code together, and submitting a joint report.

44 | 48 |

Ordinarily, all team members will receive the same grade for an assignment - though we reserve the right, in case of egregiously unequal contributions, to assign different grades to different team members. You can contact us in confidence in the event of unequal collaborations. Please ask if you have any questions about the collaboration policy.

49 |

Late Day Policy

50 |

A grading penalty will be applied to late programming assignments. We recognize that students may face unusual circumstances and require some flexibility in the course of the quarter; therefore each student will be granted a total of four free late (calendar) days to use as he or she sees fit. Once these late days are exhausted, any assignment turned in late will be penalized 10% per late day. Each 24 hours or part thereof that a assignment is late uses up one full late day. Late days may be used for the final project report.

51 | 52 |

When students collaborate on an assignment, and the assignment is 53 | submitted late, late days are deducted from each team member's 54 | balance. Altruistic team members are allowed to "donate" late days to 55 | a collaborator if they wish. 56 |

57 | 66 |

Honor Code

67 | 68 |

You should not look for programming assignment answers elsewhere. If material is taken from elsewhere, then you should acknowledge it. For programming assignments, you are not permitted to get programming help from people other than your partner. Normally, you are permitted to use pre-existing code, but you must acknowledge code that you have taken from other sources. In general, it is appropriate to make use of libraries, etc. for the final project, as needed, but it is normally not appropriate to use code from others in the programming assignments, except for ancillary functions or extra credit investigations. In these and other respects, we will act and expect you to act according to the Stanford Honor Code.

69 |

Regrading Requests

70 |

If you feel you deserved a better grade on an assignment, you may submit a regrade request in writing to the TA responsible. Your request should briefly summarize why you feel the original grade was unfair. Your TA will take a day or two to reevaluate your assignment, and then issue a decision. If you're still not satisfied, you can then appeal (again, in writing) to the course instructor, Chris Manning.

71 |

Note that in regrading an assignment, we may reevaluate any part of it, not just the part you bring to our attention.

72 |

Students with Documented Disabilities

73 |

Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty dated in the current quarter in which the request is being made. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. The OAE is located at 563 Salvatierra Walk (650-723-1066, http://studentaffairs.stanford.edu/oae). 74 |

75 |

Credit/No credit enrollment

76 |

If you're doing the class credit/no credit then there are no special changes to the workload versus people registered for a grade. You are graded on work as usual, per standard Stanford rules. The only difference is that providing you reach a C- standard in your work, it will simply be graded as CR. (So maybe you can take it a little bit easier.) 77 |

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10 | 11 | 30 | 31 |

Welcome to CS224N!

32 | 33 |

Dear CS224N Students,

34 | 35 | Welcome to CS224N: Natural Language Processing! Here's an overview of how we plan on running the course this quarter.

36 | 37 |

Lectures:

38 | 39 |

Lectures will be held 40 | in Skilling Auditorium. Attendance is encouraged as interactive tutorials and discussions will occur during lecture times.

41 | 42 |

Course Website:

43 | 44 |

The class website can be found 45 | at http://cs224n.stanford.edu 46 | (a.k.a., http://web.stanford.edu/class/cs224n/), where we will post all the course material this quarter including course announcements, so please be sure to frequently check for updates. A tentative syllabus is already up, so be sure to check it out if interested.

47 | 48 |

Further, we will be using OpenEdX for additional videos and weekly quizzes, which can be found here.

49 | 50 |

Discussion Forum and Contact Staff:

51 | 52 |

We will also be using Piazza as the primary tool for discussions this quarter. You can find the class webpage on Piazza at https://piazza.com/stanford/fall2015/cs224n. Please post all questions to Piazza including private messages to instructors (Piazza allows for posts to be made visible only to instructors) as those posts will be responded to first. E-mails sent to the staff should be sent to the staff mailing list (cs224n-aut1516-staff@lists.stanford.edu) instead of individual staff e-mail address.

53 | 54 |

Please don't hesitate to contact us if you have any questions. Looking forward to a great quarter!

55 | 56 | 57 |

This year's final project reports are online now!

58 | 59 | 60 | 61 |

62 |

Course Description

63 | 64 |

65 | This course introduces the fundamental concepts and ideas in natural language processing (NLP), otherwise known as computational linguistics. Ever wondered how Google Translate works, or how companies do automated resume processing? Want to build a computer that understands language? This course is for you. It develops an in-depth understanding of both algorithms for processing linguistic information and the underlying computational properties of natural languages. We consider word-level, syntactic, and semantic processing from both a linguistic and an algorithmic perspective, aiming to get up to speed with current research in the area. The course focuses on modern quantitative techniques in NLP -- using large corpora, statistical models for acquisition, disambiguation, and parsing -- and the construction of representative systems.

66 | 67 |

Understanding language is a very complex thing -- but 68 | something that humans are amazingly good at: 69 |

70 | 71 |

xkcd 1576: I could
 73 |         care less

75 | 76 |
84 | 85 | 90 | 91 |

92 | 93 |

94 | 95 |

Course Information

96 | 97 |
98 | 99 | 100 | 101 | 102 | 103 | 104 | 109 | 110 | 111 | 114 | 115 | 116 | 117 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 137 | 138 | 139 |
Lectures: Tue/Thur 3:00-4:20
Location: 105 | 106 | Skilling Auditorium 107 | 108 |
112 | 113 |
118 | 119 |
Instructor:Chris Manning
TAs: 128 | Danqi Chen
129 | Mihail Eric
130 | Jade Huang
131 | Neha Nayak
132 | Ashwin Paranjape
133 | Chris Billovits
134 | Nishith Khandwala
135 | 136 |
140 | 141 | 142 | 143 |

144 | Web: 145 | http://cs224n.stanford.edu/ 146 |

147 | 148 | 149 |

Piazza: CS224N forum
150 | Post questions, find project partners, etc.

151 | 152 | 153 |

154 | Staff mailing list:
155 | cs224n-aut1516-staff@lists.stanford.edu
156 |

157 | 158 |

159 | AFS space:
160 | /afs/ir/class/cs224n/
161 |

162 | 163 |

164 | Stanford computer cluster: 165 | Farmshare
166 | 167 |

168 | 169 |

170 | Office Hours:
171 | Chris Manning: Tue 11am-12pm, Gates 248
172 | Danqi Chen: Thu 4:30-6:30pm, Gates 234
173 | Mihail Eric: Wed 10am-12pm, Tech Lounge (Lathrop Library 1st Floor)
174 | Jade Huang: Thu 10:30am-12:30pm, Tech lounge (Lathrop Library 1st Floor)
175 | Neha Nayak: Wed 7-9pm, Huang Basement
176 | Ashwin Paranjape: Mon 2-4pm, Tech Lounge (Lathrop Library 1st Floor)
177 | Chris Billovits: Fri 1-3pm, Tech Lounge (Lathrop Library 1st Floor)
178 | Nishith Khandwala: Fri 3-5pm, Huang Basement

179 | Coding Session: Mon 6:10-9:10pm, GESB131 180 |

181 | 182 |

Project Guidelines

183 |

184 | Project Ideas

185 |

Past 186 | Projects

187 | 188 | 189 | 190 |
195 | 196 | 197 | 198 | 199 |
200 | 201 | 202 | 203 | 205 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | -------------------------------------------------------------------------------- /participation.shtml: -------------------------------------------------------------------------------- 1 | 2 | 3 |

4 | 5 | You can check your late days and 6 | participation here. 7 | The policy is described in the grading page. 8 | 9 |

10 | 11 | 13 | 14 | -------------------------------------------------------------------------------- /scripts/md5.js: -------------------------------------------------------------------------------- 1 | /* 2 | CryptoJS v3.1.2 3 | code.google.com/p/crypto-js 4 | (c) 2009-2013 by Jeff Mott. All rights reserved. 5 | code.google.com/p/crypto-js/wiki/License 6 | */ 7 | var CryptoJS=CryptoJS||function(s,p){var m={},l=m.lib={},n=function(){},r=l.Base={extend:function(b){n.prototype=this;var h=new n;b&&h.mixIn(b);h.hasOwnProperty("init")||(h.init=function(){h.$super.init.apply(this,arguments)});h.init.prototype=h;h.$super=this;return h},create:function(){var b=this.extend();b.init.apply(b,arguments);return b},init:function(){},mixIn:function(b){for(var h in b)b.hasOwnProperty(h)&&(this[h]=b[h]);b.hasOwnProperty("toString")&&(this.toString=b.toString)},clone:function(){return this.init.prototype.extend(this)}}, 8 | q=l.WordArray=r.extend({init:function(b,h){b=this.words=b||[];this.sigBytes=h!=p?h:4*b.length},toString:function(b){return(b||t).stringify(this)},concat:function(b){var h=this.words,a=b.words,j=this.sigBytes;b=b.sigBytes;this.clamp();if(j%4)for(var g=0;g>>2]|=(a[g>>>2]>>>24-8*(g%4)&255)<<24-8*((j+g)%4);else if(65535>>2]=a[g>>>2];else h.push.apply(h,a);this.sigBytes+=b;return this},clamp:function(){var b=this.words,h=this.sigBytes;b[h>>>2]&=4294967295<< 9 | 32-8*(h%4);b.length=s.ceil(h/4)},clone:function(){var b=r.clone.call(this);b.words=this.words.slice(0);return b},random:function(b){for(var h=[],a=0;a>>2]>>>24-8*(j%4)&255;g.push((k>>>4).toString(16));g.push((k&15).toString(16))}return g.join("")},parse:function(b){for(var a=b.length,g=[],j=0;j>>3]|=parseInt(b.substr(j, 10 | 2),16)<<24-4*(j%8);return new q.init(g,a/2)}},a=v.Latin1={stringify:function(b){var a=b.words;b=b.sigBytes;for(var g=[],j=0;j>>2]>>>24-8*(j%4)&255));return g.join("")},parse:function(b){for(var a=b.length,g=[],j=0;j>>2]|=(b.charCodeAt(j)&255)<<24-8*(j%4);return new q.init(g,a)}},u=v.Utf8={stringify:function(b){try{return decodeURIComponent(escape(a.stringify(b)))}catch(g){throw Error("Malformed UTF-8 data");}},parse:function(b){return a.parse(unescape(encodeURIComponent(b)))}}, 11 | g=l.BufferedBlockAlgorithm=r.extend({reset:function(){this._data=new q.init;this._nDataBytes=0},_append:function(b){"string"==typeof b&&(b=u.parse(b));this._data.concat(b);this._nDataBytes+=b.sigBytes},_process:function(b){var a=this._data,g=a.words,j=a.sigBytes,k=this.blockSize,m=j/(4*k),m=b?s.ceil(m):s.max((m|0)-this._minBufferSize,0);b=m*k;j=s.min(4*b,j);if(b){for(var l=0;l>>32-j)+k}function m(a,k,b,h,l,j,m){a=a+(k&h|b&~h)+l+m;return(a<>>32-j)+k}function l(a,k,b,h,l,j,m){a=a+(k^b^h)+l+m;return(a<>>32-j)+k}function n(a,k,b,h,l,j,m){a=a+(b^(k|~h))+l+m;return(a<>>32-j)+k}for(var r=CryptoJS,q=r.lib,v=q.WordArray,t=q.Hasher,q=r.algo,a=[],u=0;64>u;u++)a[u]=4294967296*s.abs(s.sin(u+1))|0;q=q.MD5=t.extend({_doReset:function(){this._hash=new v.init([1732584193,4023233417,2562383102,271733878])}, 15 | _doProcessBlock:function(g,k){for(var b=0;16>b;b++){var h=k+b,w=g[h];g[h]=(w<<8|w>>>24)&16711935|(w<<24|w>>>8)&4278255360}var b=this._hash.words,h=g[k+0],w=g[k+1],j=g[k+2],q=g[k+3],r=g[k+4],s=g[k+5],t=g[k+6],u=g[k+7],v=g[k+8],x=g[k+9],y=g[k+10],z=g[k+11],A=g[k+12],B=g[k+13],C=g[k+14],D=g[k+15],c=b[0],d=b[1],e=b[2],f=b[3],c=p(c,d,e,f,h,7,a[0]),f=p(f,c,d,e,w,12,a[1]),e=p(e,f,c,d,j,17,a[2]),d=p(d,e,f,c,q,22,a[3]),c=p(c,d,e,f,r,7,a[4]),f=p(f,c,d,e,s,12,a[5]),e=p(e,f,c,d,t,17,a[6]),d=p(d,e,f,c,u,22,a[7]), 16 | c=p(c,d,e,f,v,7,a[8]),f=p(f,c,d,e,x,12,a[9]),e=p(e,f,c,d,y,17,a[10]),d=p(d,e,f,c,z,22,a[11]),c=p(c,d,e,f,A,7,a[12]),f=p(f,c,d,e,B,12,a[13]),e=p(e,f,c,d,C,17,a[14]),d=p(d,e,f,c,D,22,a[15]),c=m(c,d,e,f,w,5,a[16]),f=m(f,c,d,e,t,9,a[17]),e=m(e,f,c,d,z,14,a[18]),d=m(d,e,f,c,h,20,a[19]),c=m(c,d,e,f,s,5,a[20]),f=m(f,c,d,e,y,9,a[21]),e=m(e,f,c,d,D,14,a[22]),d=m(d,e,f,c,r,20,a[23]),c=m(c,d,e,f,x,5,a[24]),f=m(f,c,d,e,C,9,a[25]),e=m(e,f,c,d,q,14,a[26]),d=m(d,e,f,c,v,20,a[27]),c=m(c,d,e,f,B,5,a[28]),f=m(f,c, 17 | d,e,j,9,a[29]),e=m(e,f,c,d,u,14,a[30]),d=m(d,e,f,c,A,20,a[31]),c=l(c,d,e,f,s,4,a[32]),f=l(f,c,d,e,v,11,a[33]),e=l(e,f,c,d,z,16,a[34]),d=l(d,e,f,c,C,23,a[35]),c=l(c,d,e,f,w,4,a[36]),f=l(f,c,d,e,r,11,a[37]),e=l(e,f,c,d,u,16,a[38]),d=l(d,e,f,c,y,23,a[39]),c=l(c,d,e,f,B,4,a[40]),f=l(f,c,d,e,h,11,a[41]),e=l(e,f,c,d,q,16,a[42]),d=l(d,e,f,c,t,23,a[43]),c=l(c,d,e,f,x,4,a[44]),f=l(f,c,d,e,A,11,a[45]),e=l(e,f,c,d,D,16,a[46]),d=l(d,e,f,c,j,23,a[47]),c=n(c,d,e,f,h,6,a[48]),f=n(f,c,d,e,u,10,a[49]),e=n(e,f,c,d, 18 | C,15,a[50]),d=n(d,e,f,c,s,21,a[51]),c=n(c,d,e,f,A,6,a[52]),f=n(f,c,d,e,q,10,a[53]),e=n(e,f,c,d,y,15,a[54]),d=n(d,e,f,c,w,21,a[55]),c=n(c,d,e,f,v,6,a[56]),f=n(f,c,d,e,D,10,a[57]),e=n(e,f,c,d,t,15,a[58]),d=n(d,e,f,c,B,21,a[59]),c=n(c,d,e,f,r,6,a[60]),f=n(f,c,d,e,z,10,a[61]),e=n(e,f,c,d,j,15,a[62]),d=n(d,e,f,c,x,21,a[63]);b[0]=b[0]+c|0;b[1]=b[1]+d|0;b[2]=b[2]+e|0;b[3]=b[3]+f|0},_doFinalize:function(){var a=this._data,k=a.words,b=8*this._nDataBytes,h=8*a.sigBytes;k[h>>>5]|=128<<24-h%32;var l=s.floor(b/ 19 | 4294967296);k[(h+64>>>9<<4)+15]=(l<<8|l>>>24)&16711935|(l<<24|l>>>8)&4278255360;k[(h+64>>>9<<4)+14]=(b<<8|b>>>24)&16711935|(b<<24|b>>>8)&4278255360;a.sigBytes=4*(k.length+1);this._process();a=this._hash;k=a.words;for(b=0;4>b;b++)h=k[b],k[b]=(h<<8|h>>>24)&16711935|(h<<24|h>>>8)&4278255360;return a},clone:function(){var a=t.clone.call(this);a._hash=this._hash.clone();return a}});r.MD5=t._createHelper(q);r.HmacMD5=t._createHmacHelper(q)})(Math); 20 | -------------------------------------------------------------------------------- /selfcheck.shtml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 20 | 21 |
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30 | Report your participation here. 31 | The policy is described in the grading page. 32 |

33 | 34 | 35 | 36 | 37 | 38 | -------------------------------------------------------------------------------- /syllabus.shtml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 |
6 |

Calendar

7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 24 | 25 | 31 | 33 | 34 | 35 | 36 | 37 | 38 | 44 | 45 | 49 | 50 | 51 | 52 | 53 | 54 | 59 | 60 | 72 | 74 | 75 | 76 | 77 | 78 | 82 | 83 | 87 | 88 | 89 | 90 | 91 | 92 | 99 | 100 | 110 | 111 | 112 | 113 | 114 | 115 | 119 | 120 | 123 | 124 | 125 | 126 | 127 | 128 | 133 | 145 | 146 | 147 | 148 | 149 | 150 | 156 | 157 | 162 | 163 | 164 | 165 | 166 | 167 | 172 | 173 | 177 | 178 | 179 | 180 | 181 | 182 | 186 | 187 | 191 | 192 | 193 | 194 | 195 | 196 | 201 | 202 | 207 |
208 | 209 | 210 | 211 |
MonTueWedThuFri
Sep 21Sep 22 21 |
22 | Lecture 1: Introduction
23 |
Sep 23Sep 24 26 | 27 |
28 | Lecture 2: Word Alignment Models for Statistical MT 29 | PA1 out 30 |
Sep 25 32 |
Sep 28 39 | Sep 29 40 |
41 | Lecture 3: Machine Translation: Word Alignment, Parallel 42 | Corpora, Decoding, Evaluation 43 |
Sept 30Oct 1 46 |
47 | Lecture 4: Modern MT Systems (Phrase-based, Syntactic) 48 |
Oct 2
Oct 5Oct 6 55 |
56 | Lecture 5: N-Grams, Final Project Discussion 57 | 58 |
Oct 7Oct 8 61 | 62 | PA2 out 63 | 64 | 65 |
66 | Lecture 6: Syntax and parsing 67 | 68 | PA1 due 69 | 70 | 71 |
Oct 9 73 |
Oct 12Oct 13 79 |
80 | Lecture 7: Competitive Grammar Writing I 81 |
Oct 14Oct 15 84 |
85 | Lecture 8: Competitive Grammar Writing II 86 |
Oct 16
Oct 19Oct 20 93 |
94 | Lecture 9: Dependency Parsing 95 | 96 | Final project proposal due 97 | 98 |
Oct 21Oct 22 101 | 102 | PA2 due 103 | 104 |
105 | Lecture 10: Coreference Resolution 106 | 107 | PA3 out 108 | 109 |
Oct 23
Oct 26Oct 27 116 |
117 | Lecture 11: Coreference Resolution II / Classifiers 118 |
Oct 28Oct 29
121 | Lecture 12: Softmax / MaxEnt (Sequence) Classifiers 122 |
Oct 30
Nov 2Nov 3 129 |
130 | Lecture 13: Sequence Classifiers for POS & NER / Deep 131 | Learning for NLP: Motivations 132 |
Nov 4 134 | Nov 5 135 | 136 | PA3 due 137 | 138 |
139 | Lecture 14: Deep Learning for NLP: Word 140 | Representations & NER 141 | 142 | PA4 out 143 | 144 |
Nov 6
Nov 9
151 |
Nov 10 152 |
153 | Lecture 15: Deep Learning for NLP: Strategy 154 | and Tree Recursive Neural Networks 155 |
Nov 11Nov 12 158 |
159 | Lecture 16: Deep Learning for NLP: Recurrent 160 | Neural Networks 161 |
Nov 13
Nov 16
168 |
Nov 17 169 |
170 | Lecture 17: Computational Semantics 171 |
Nov 18Nov 19 174 |
175 | Lecture 18: Computational Semantics 176 |
Nov 20
Nov 23Nov 24 183 |
184 | Thanksgiving
185 |
Nov 25Nov 26 188 |
189 | Thanksgiving
190 |
Nov 27
Nov 30
197 |
Dec 1 198 |
199 | Final project presentations 200 |
Dec 2Dec 3 203 |
204 | 205 | Final project presentations 206 |
Dec 4
212 | 213 |
214 | PA 4 due: Dec 4.
215 | Final project report due: Dec 6. 216 | 217 | 218 | 219 |

220 |

Syllabus

221 | 222 | 223 | 224 | 225 | 226 | 228 | 249 | 250 |
Lecture 1
Tue
227 | 9/22/15
229 | Course Introduction and Administration. Overview of NLP. Statistical Machine Translation. 230 |

231 | 232 | Lecture Slides: (1-up) (6-up)

233 | 234 | Required: 235 |
    236 |
  • If your knowledge of probability theory is limited, please read M&S 2.0-2.1.7. If that's too condensed, read the probability chapter of an intro statistics textbook, e.g. Rice, Mathematical Statistics and Data Analysis, ch. 1. 237 |
  • 238 |
239 | Optional: 240 | 248 |
251 | 252 | 253 | 254 | 255 | 256 | 257 | 259 | 297 | 298 |
Lecture 2
Thu
258 | 9/24/15
260 | Word Alignment Models for Statistical MT 261 |

262 | Assignments: 263 |
  • PA1 (Word Alignment and MT System) Out. (Find it 264 | on OpenEdX under Courseware.)
265 | 266 | Lecture Slides: (1-up) (6-up)

267 | 268 | Tutorial reading: 269 | 276 | 277 | 278 | Background: 279 | 282 | 283 | Advanced: 284 | 295 | 296 |
299 | 300 | 301 | 302 | 303 | 304 | 306 | 326 | 327 |
Lecture 3
Tue
305 | 9/29/15
307 | Machine Translation: Word Alignment, Parallel 308 | Corpora, Decoding, Evaluation

309 | 310 | Lecture Slides: (1-up) (6-up)

311 | 312 | 313 | Required:
  • J&M chapter 25
314 | Tutorial reading: 315 | 319 | Optional: 320 | 325 |
328 | 329 | 330 | 331 | 332 | 333 | 334 | 336 | 351 | 352 |
Lecture 4
Thu
335 | 10/1/15
337 | Modern MT Systems (Phrase-based, Syntactic)
338 |
339 | 340 | Lecture Slides: 341 | (1-up) 342 | (6-up)

343 | 344 | Optional: 345 | 350 |
353 | 354 | 355 | 356 | 357 | 359 | 386 | 387 |
Lecture 5
Tue
358 | 10/6/15
360 | N-Grams, Final Project Discussion

361 | 362 | Lecture Slides: 363 | (1-up) 364 | (6-up)

365 | 366 | 367 | Required: 368 | 374 | Resources: 375 | 379 | Optional: 380 | 385 |
388 | 389 | 390 | 391 |
392 | 393 | 394 | 396 | 423 | 424 |
Lecture 6
Thu
395 | 10/8/15
397 | Syntax and parsing

398 | 399 | Lecture Slides: 400 | (1-up) 401 | (6-up)

402 | 403 | 404 | Assignments: 405 |
    406 |
  • PA1 due
  • 407 |
  • PA2 (CYK-Parser) out
  • 408 |
409 |
410 | 411 | Required: 412 |
    413 |
  • Week 3 Parsing Videos
  • 414 |
  • J&M ch. 13, secs. 13.0-13.3.
  • 415 |
416 | 417 | Background: 418 |
    419 |
  • J&M ch. 12 (or M&S ch. 3). This is especially if you haven't done any linguistics courses, but even if you have, there's useful information on treebanks and part-of-speech tag sets used in NLP. 420 |
  • 421 |
422 |
425 | 426 | 427 |
428 | 429 | 430 | 432 | 450 | 451 |
Lecture 7
Tue
431 | 10/13/15
433 | Competitive Grammar Writing I

434 | 435 | Lecture Slides: 436 | (pdf)
437 | Instructions: 438 | (pdf)

439 | 440 | Required: 441 |
    442 |
  • Week 4 Parsing Videos
  • 443 |
  • J&M sec 13.4
  • 444 |
445 | Background: 446 | 449 |
452 | 453 | 454 | 455 |
456 | 457 | 458 | 460 | 475 | 476 |
Lecture 8
Thu
459 | 10/15/15
461 | Competitive Grammar Writing II

462 | Required: 463 |
    464 |
  • Week 4 Parsing Videos
  • 465 |
466 | Optional: 467 | 473 | 474 |
477 | 478 | 479 | 480 | 481 |
482 | 483 | 484 | 486 | 500 | 501 |
Lecture 9
Tue
485 | 10/20/15
487 | Dependency Parsing

488 | 489 | Lecture Slides: 490 | (1-up) 491 | (6-up)

492 | 493 | 494 | Assignments: 495 |
    496 |
  • Final Project Proposal Due. 497 |
498 |
499 |
502 | 503 | 504 | 505 |
506 | 507 | 508 | 510 | 533 | 534 |
Lecture 10
Thu
509 | 10/22/15
511 | Coreference Resolution

512 | 513 | Lecture Slides: (1-up) (6-up)

514 | 515 | 516 | Assignments: 517 |
    518 |
  • PA2 Due
  • 519 |
  • PA3 (Coreference System) out
  • 520 |
521 |

522 | Required: 523 |
    524 |
  • J&M 21.3-21.8 (or all of Chapter 21 if you wish!)
  • 525 |
526 | 527 | Optional: 528 | 532 |
535 | 536 | 537 | 538 |
539 | 540 | 541 | 543 | 553 | 554 |
Lecture 11
Tue
542 | 10/27/15
544 | Coreference Resolution II

545 | 546 | Lecture Slides: 547 | (1-up) 548 | (6-up)
549 | Intro to feature-based classifiers: (1-up) (6-up)

550 |

551 | 552 |
555 | 556 | 557 | 558 |
559 | 560 | 561 | 563 | 581 | 582 |
Lecture 12
Thu
562 | 10/29/15
564 | Softmax / MaxEnt (Sequence) Classifiers

565 | 566 | 570 | 571 | 572 | Lecture Slides: (1-up) (6-up)

573 | 574 | Optional: 575 | 579 | 580 |
583 | 584 | 585 | 586 |
587 | 588 | 589 | 591 | 601 | 602 |
Lecture 13
Tue
590 | 11/3/15
592 | Sequence Classifiers for POS & NER / Deep 593 | Learning for NLP: Motivations

594 | 595 | Lecture Slides: 596 |
    597 |
  • Maxent models continuation:(1-up) (6-up) 598 |
  • Intro to deep learning and word representations: (1-up) (6-up) 599 |
600 |
603 | 604 | 605 | 606 |
607 | 608 | 609 | 611 | 624 | 625 |
Lecture 14
Thu
610 | 11/5/15
612 | Deep Learning for NLP: Word representations & NER

613 | 614 | Lecture Slides: (1-up) (6-up)

615 | 616 | 617 | Assignments: 618 |
    619 |
  • PA3 Due
  • 620 |
  • PA4 (Deep Learning Sequence Model or Dependency Parsing) out
  • 621 |
622 |
623 |
626 | 627 | 628 | 629 |
630 | 631 | 632 | 634 | 635 | 663 | 664 |
Lecture 15
Tue
633 | 11/10/15
636 | Deep Learning for NLP: Strategy & Tree 637 | Recursive Neural Networks

638 | 639 | Lecture Slides: 640 | (1-up) 641 | (6-up) 642 |

643 | Optional reading (from easiest to hardest!): 644 | 659 | 660 | 661 | 662 |
665 | 666 | 667 |
668 | 669 | 670 | 672 | 673 | 679 | 680 |
Lecture 16
Thu
671 | 11/12/15
674 | Deep Learning for NLP: Recurrent Neural Networks

675 | Lecture Slides: 676 | (1-up) 677 | (6-up) 678 |
681 | 682 | 683 | 684 |
685 | 686 | 687 | 689 | 704 | 705 |
Lecture 17
Tue
688 | 11/17/15
690 | Computational Semantics

691 | Lecture Slides: 692 | [1-up] 693 | [6-up] 694 |

695 | Background links: 696 | [Background 697 | on knowledge navigator] 698 | [SHRDLU] 699 | [Google 700 | app 2015] 701 | 702 | 703 |
706 | 707 | 708 | 709 | 719 | 720 | 721 | 722 |
723 | 724 | 725 | 727 | 750 | 751 | 752 |
Lecture 18
Thu
726 | 11/19/15
728 | Computational Semantics

729 | Lecture Slides: 730 | first part (1-up) 731 | (6-up); 732 | second part (1-up). 733 |

734 | Required: 735 |

738 |

739 | Lecture Slides: 740 | third part (1-up). 741 | 742 | 743 | 747 | 748 | 749 |
753 | 754 | 755 | 756 | 759 | 760 |
  757 | Thanksgiving Break 758 |
761 | 762 | 763 | 764 |
765 | 766 | 767 | 769 | 774 | 775 |
Lecture 19
Tue
768 | 12/1/15
770 | Final project presentations

771 | Schedule 772 |

773 |
776 | 777 |
778 | 779 | 780 | 782 | 786 | 787 |
Lecture 20
Tue
781 | 12/3/15
783 | Final project presentations

784 | Schedule 785 |
788 | 822 | 823 | 830 | 831 | 832 | 833 |
834 | 835 | 836 | 837 | 839 | 844 | 845 | 846 | 847 | 848 | 849 | 850 | -------------------------------------------------------------------------------- /textbook.shtml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 |
5 |

Textbook and Readings

6 |

The required text is:

7 |

Daniel Jurafsky and James H. Martin. 2008. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Second Edition. Prentice Hall.

8 |
11 |

Of course, we're also fond of:

12 |

Christopher D. Manning and Hinrich Schütze. 1999. Foundations of Statistical Natural Language Processing. MIT Press.

13 |
    14 |
  • Buy it at the Stanford Bookstore or Amazon.
  • 15 |
  • You can read the text online from the Stanford network! It's referred to as M&S in the syllabus. While a bit older, it also has good and often distinct coverage of many topics. Please see http://nlp.stanford.edu/fsnlp/ for supplementary information about the text, including errata, and pointers to online resources.
  • 16 |
17 |

Other useful reference texts for NLP are:

18 |
    19 |
  • Steven Bird. 2009. Natural Language Processing with Python. O'Reilly. (Free on SearchWorks)
  • 20 |
  • Philipp Koehn. 2010. Statistical Machine Translation. Cambridge.
  • 21 |
  • Yoshua Bengio. 2009. Learning Deep Architectures for AI. Technical Report. (Free from Stanford network)
  • 22 |
  • Frederick Jelinek. 1998. Statistical Methods for Speech Recognition. MIT Press.
  • 23 |
  • James Allen. 1995. Natural Language Understanding. Benjamin/Cummings, 2ed.
  • 24 |
25 |

Other papers with relevant material will be posted on the syllabus, as will lecture slides.

26 | 27 | --------------------------------------------------------------------------------