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.

Office Hours (in NLP lab (7LW1 CEPSR) unless otherwise stated):
Monday 9.30am-11am, Emily Li
Monday 12.30pm-2pm, Arthur Chen
Wednesday 4pm-5.30pm, Alyssa Huang
Friday 11.10am-12.40pm, David Wan


Past midterms for the class are here: fall 2011, fall 2012, fall 2013, fall 2014, fall 2017, spring 2018.

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

Date Topics Video Lectures References Flipped Classroom Materials
Week 1 (January 20th-24th) Introduction to NLP,
Language Modeling (Slides: we will cover slides 1-50 inclusive)
Video lectures in Courseworks: All of Module 1-2; All of Module 3; Sections 4.1, 4.2 in Module 4. Sections 1.1-1.4.1 inclusive of Notes on language modeling (required reading).
Questions (part 1), Solutions (part 1) Questions (part 2), Solutions (part 2)
Week 2 (January 27th-31st) Tagging, and Hidden Markov Models (Slides) All videos in Module 6 in courseworks: The tagging problem (10:01) to Summary (1:50) inclusive. Notes on tagging problems, and hidden Markov models (required reading)
Questions, Solutions
Week 3 (February 3rd-7th) Log-Linear Models (Slides) All videos in Module 15 on Courseworks. Notes on Log-Linear Models (required reading)
Questions, Solutions, Past midterm question
Week 4 (February 10th-14th) 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 5 (February 17th-21st) 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 6 (February 24th-February 28th) 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 on CRFs, solutions are in section 4 of this note. Additional questions, Solutions
Week 7 (March 2nd-March 6th) Feedforward Neural Networks (Slides) Module 22 videos in Courseworks.

Notes on Feedforward Neural Networks (required reading)
Questions, Solutions
Week 9 (March 26th-March 31st) Computational Graphs, and Backpropagation (Slides) Module 23 videos in Courseworks.

Notes on Computational Graphs, and Backpropagation (required reading)
Questions, Solutions
Week 10 (April 2nd-7th) Word Embeddings in Feedforward Networks; Tagging and Dependency Parsing using Feedforward Networks (Slides) Module 24 videos in Courseworks.

Week 11 (April 9th-14th) Recurrent Networks, and LSTMs, for NLP (Slides) Module 25 in Courseworks. Questions, Solutions
Week 12 (April 16th-21st) Recurrent Networks, and attention, for statistical machine translation (Slides) Module 26 in Courseworks. Questions, Solutions
Week 13 (April 23rd-28th) Brown Clustering, and Word2Vec (Brown Clustering Slides), (Word2Vec Slides). Module 18 in Courseworks. Word2Vec paper (we will cover in the flipped classroom section) Questions, Solutions
Week 14 (April 30th-May 4th) TBD