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:
Justin Zhao: Mondays, 5:30pm-7pm (jxz2101@columbia.edu)
Noura Farra: Tuesdays, 4pm-5:30pm (noura@cs.columbia.edu)
Richard Chiou: Thursdays, 2:30pm-4pm (rc2758@columbia.edu)
Mohammad Sadegh Rasooli: Fridays, 4pm-5:30pm (rasooli@cs.columbia.edu)

Office hours will be held at the NLP lab (7th floor CEPSR, room 7LW1)

Announcements:


The mid-term will be on Thursday, March 5th from 1:10-2:25pm, in 702 Hamilton.

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


Lectures: Video lectures are on Coursera: once you have signed in to Coursera, follow this link.
Date Topics Video Lectures References Flipped Classroom Materials
Week 1 (Jan 19-23) Introduction to NLP,
Language Modeling (part 1)
Week 1 Coursera videos from Introduction (Part 1) (11:17) to Markov Processes (Part 2) (7:12) inclusive. Sections 1.1 and 1.2 of Notes on language modeling (required reading)
Questions, Solutions
Week 2 (Jan 28-Feb 3) Language Modeling (part 2) Week 1 Coursera videos from Trigram Language Models (9:40) to Summary (2:31) inclusive.
NOTE: Columbia has cancelled classes on Jan 27th due to the weather. So flipped classrooms for week 2 will be Thursday Jan 29th and Tuesday Feb 3rd. We'll continue with the Thursday/Tuesday schedule for the next few weeks.
Sections 1.3 and 1.4 of Notes on language modeling (required reading)
Questions, Solutions
Week 3 (Feb 4-10) Tagging, and Hidden Markov Models Week 2 Coursera videos from The tagging problem (10:01) to Summary (1:50) inclusive. Notes on tagging problems, and hidden Markov models (required reading)
Questions, Solutions
Week 4 (Feb 11-17) Parsing, and Context-free Grammars Week 3 - Parsing, and Context-free Grammars Coursera videos from Introduction (0:28) to Examples of Ambiguity (5:56) inclusive. Questions, Solutions
Week 5 (Feb 18-24) Probabilistic Context-free Grammars Week 3 - Probabilistic Context-Free Grammars (PCFGs) Coursera videos from Introduction (1:12) to The CKY Parsing Algorithm (Part 3) (10:07) inclusive. Notes on Probabilistic Context-Free Grammars (required reading)
Questions, Solutions
Week 6 (Feb 25-March 3) Lexicalized PCFGs All Week 4 Coursera videos, covering Weaknesses of PCFGs and Lexicalized PCFGs Notes on Lexicalized Probabilistic Context-Free Grammars (required reading)
Questions, Solutions
Thursday March 5th Mid-term
Week 7 (March 9-13) The IBM Translation Models Week 5 - Introduction to Machine Translation (MT) Coursera videos, covering Opening Comments (0:25) to Introduction to Statistical MT (12:31) inclusive. Week 5 - The IBM Translation Models Coursera videos, covering Introduction (3:24) to Summary (1:48) inclusive. Notes on Statistical Machine Translation: IBM Models 1 and 2 (required reading)
Questions, Solutions
Week 8 (March 23-March 27) Phrase-Based Translation Models All Week 6 Coursera videos, covering Phrase-Based Translation Models and Decoding of Phrase-Based Translation Models. Notes on Phrase-Based Translation Models (required reading)
Questions, Solutions
Week 9 (March 30-April 3) Log-Linear Models All Week 7 Coursera videos, covering Log-Linear Models.

Notes on Log-Linear Models (required reading)
Questions, Solutions
Week 10 (April 6-10) Log-Linear Models for Tagging, and Global Linear Models Week 8 - Log-Linear Models for Tagging (MEMMs) Coursera videos, and Week 9 - Global Linear Models (GLMs) Coursera videos.

Notes on MEMMs (Log-Linear Tagging Models) (required reading)
Questions, Solutions
Week 11 (April 13-17) Global Linear Models for Tagging Week 10 - GLMs for Tagging Coursera videos. Questions, Solutions
Week 12 (April 20-24) Global linear models for dependency parsing Week 10 - GLMs for Dependency Parsing Coursera videos, and and Week 9 - Unsupervised Learning: Brown Clustering Coursera videos.

Questions, Solutions
Week 13 (April 27-May 1st) Preparation for Final This week we will give over the fall 2014 final in the flipped classroom sections. Please spend some time going over the final before you come to the section.