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,
contextfree grammars, and probabilistic CFGs

Note on PCFGs (required reading)

9/20 
Parsing,
contextfree 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 
Phrasebased translation models

Note on phrasebased models (required reading)
Slides from the tutorial by Philipp Koehn

10/16 
Phrasebased translation models: the decoding algorithm


10/18 
Midterm (in class)


10/23 
Reordering for statistical MT


10/25 
Loglinear models

Note on loglinear models (required reading).

11/1 
Loglinear 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 wordclustering algorithm


11/29 
Semisupervised learning for
wordsense disambiguation,
and
cotraining for namedentity 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.)
