COMS W4705: Natural Language Processing

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Instructor: Michael Collins
Time & Location: Tues & Thurs 4.10-5.25, 1024 Mudd
Office Hours:Thursdays 2-3pm, CEPSR (Schapiro) 723

TAs: Please send all questions to nlpfall2012.columbia at gmail.
Alexander Rush [ar3148] (office hours Tuesday 1.30-2.30, in 701 CEPSR)
Yuan Du [yd2234] (office hours Monday 1-3, in TA room in Mudd)
Dhinesh Dhanasekaran [dkd2110] (office hours Wednesday 10-12, in TA room in Mudd)

Announcements:

December 12th: The final is on December 18th and will cover all material from the class, excluding the last lecture (on 12/6). Here is an old final: f1 (Note that the first two questions were not covered this year). Good luck!
October 15th: The midterm is on October 18th and will cover up to the last parsing lecture. Here are some old midterms: m1, m2, and m3. (Note that some of the questions in these old tests are on topics that are not covered in this years midterm). Good luck!
September 6th: the classroom for the course is now 1024 Mudd.

Lectures:

Date	Topic	References
9/4	Introduction and Overview
9/6	Language Modeling	Notes on language modeling (required reading)
9/11	Tagging, and Hidden Markov Models	Notes on HMMs (Required reading)
9/13	Tagging, and Hidden Markov Models (continued)
9/18	Parsing, context-free grammars, and probabilistic CFGs	Note on PCFGs (required reading)
9/20	Parsing, context-free grammars, and probabilistic CFGs (continued)
9/25	Lexicalized probabilistic CFGs
9/25	Lexicalized probabilistic CFGs (continued)	Note on Lexicalized PCFGs (required reading)
10/2	Guest lecture by Nizar Habash
10/4	Machine translation part 1	(Note: we didn't cover the final section, on evaluation using BLEU, but I've kept it in the slides in case it's of interest.)
10/9	Machine translation part 2	Note on IBM Models 1 and 2 (required reading)
10/11	Phrase-based translation models	Note on phrase-based models (required reading) Slides from the tutorial by Philipp Koehn
10/16	Phrase-based translation models: the decoding algorithm
10/18	Mid-term (in class)
10/23	Reordering for statistical MT
10/25	Log-linear models	Note on log-linear models (required reading).
11/1	Log-linear tagging (MEMMs)
11/8	Global linear models
11/13	Global linear models part II
11/15	Global linear models part III
11/20	Guest lecture: Joint Decoding	Tutorial on dual decomposition
11/27	The Brown word-clustering algorithm
11/29	Semi-supervised learning for word-sense disambiguation, and cotraining for named-entity detection
12/3	The EM algorithm for Naive Bayes	Notes on the EM algorithm for Naive Bayes (Sections 4 and 6 provide useful technical background, but can be safely skipped.)