COMS W4705: Natural Language Processing

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Instructor: Michael Collins
Time & Location: This semester the class will be taught in a flipped classroom format: details are here.

TAs and Office Hours:

Tom Effland - Friday noon-1.30pm, CEPSR 721
Danning Zheng - Thursday 4.15pm-5.45pm, computer science TA room
Xingyu Chen - Tuesday 2.30pm-4pm, computer science TA room


Past midterms for the class are here: fall 2011, fall 2012, fall 2013, fall 2014.

Lectures: Video lectures are all available by logging in to Courseworks 2.

Date Topics Video Lectures References Flipped Classroom Materials
Week 1 (January 17th-23rd) Introduction to NLP,
Language Modeling (Slides)
Modules 1-5 in courseworks. Sections 1.1-1.4 of Notes on language modeling (required reading).
Questions (part 1), Solutions (part 1) Questions (part 2), Solutions (part 2)
Week 2 (Jan 24th-30th) Tagging, and Hidden Markov Models (Slides) Week 2 Coursera videos from The tagging problem (10:01) to Summary (1:50) inclusive. (Module 6 in courseworks.) Notes on tagging problems, and hidden Markov models (required reading)
Questions, Solutions
Week 3 (Jan 31st-Feb 6th) Parsing, and Context-free Grammars (Slides) Courseworks videos: [1] All of Module 7; [2] Module 8, sections 8-1 to 8-3 inclusive Questions, Solutions
Week 4 (Feb 7th-13th) Probabilistic Context-free Grammars (continued), and lexicalized context-free grammars (Slides part 1) (Slides part 2), (Slides part 3) Courseworks videos: [1] Module 8, sections 8-4 to 8-6; [2] All of Module 9; [3] All of Module 10 Notes on Probabilistic Context-Free Grammars (required reading)
Questions, Solutions
Week 5 (Feb 14th-20th) Log-Linear Models (Slides) All videos in Module 15 on Courseworks. Notes on Log-Linear Models (required reading)
Questions, Solutions
Week 6 (Feb 21st-Feb 27th) Log-Linear Models for Tagging, and for history-based parsing (Slides part 1), (Slides part 2). Modules 16 and 17 in courseworks. Notes on MEMMs (Log-Linear Tagging Models) (required reading)
Questions, Solutions
Week 7 (Feb 28th-March 6th) Feedforward Neural Networks (Slides) Module 22 videos in Courseworks.

Notes on Feedforward Neural Networks (required reading)
Questions, Solutions
Week 8 (March 26th-30th) Computational Graphs, and Backpropagation (Slides) Module 23 videos in Courseworks.

Notes on Computational Graphs, and Backpropagation (required reading)
Questions, Solutions
Week 9 (April 2nd-April 6th) Word Embeddings in Feedforward Networks; Tagging and Dependency Parsing using Feedforward Networks (Slides) Module 24 videos in Courseworks.

Week 10 (April 9th-April 13th) The IBM Translation Models (Slides) Modules 11-12 in Courseworks. Notes on Statistical Machine Translation: IBM Models 1 and 2 (required reading)
Questions, Solutions
Week 11 (April 16th-April 20th) Recurrent Networks, and LSTMs, for NLP (Slides) Module 25 in Courseworks. Questions, Solutions
Week 12 (April 23rd-April 27th) Recurrent Networks, and attention, for statistical machine translation (Slides) Module 26 in Courseworks. Questions, Solutions