Instructor:
Dan Gildea (Office hours: Wed 2:003:00pm or by appointment CSB 730)
TA: Yuncheng Li (Office hours: Thu 2:003:00pm or by appointment CSB 617, Availability at homepage)
Location: Tu/Th 3:254:40pm, CSB 703.
Homeworks
Text: Jurafsky and Martin, Speech and Language Processing, 2nd edition.

We will have a short python help session in CSB 703
at 12:45 on Friday 9/6.
Syllabus
On  we will cover  which means that after class you will understand  if before class you have read 
9/3 
Perceptron for Tagging 
NLP = ML + DP
 Collins 2002 
9/5 
Hidden Markov Models 
viterbi, forward
 JM 6 
9/10 
Variational Bayes 
digamma, expected log probs
 JM 5; Johnson 2007 
9/12 
CRFs 
f  E[f]
 McCallum 2001 
9/17 
Ngrams 
GoodTuring, Katz, KneserNey
 JM 4 
9/19 
Signal Processing for Speech 
MFCC
 JM 9 
9/24 
Forced Alignment 
Deep learning
 Hinton 2012 
9/26 
Large Vocabulary Speech Recognition 
beam search, multistack decoding
 JM 10 
10/1 
Max Margin Learning 
subgradients
 Tsochantaridis 2004, Ratliff 2006 
10/3 
Context Free Grammars 
highest prob string, infinite trees
 JM 13 
10/8 
Fall Break 
 
10/10 
Treebanks 
right node raising
 JM 12 
10/15 
Parsing 
cyk, earley, posterior decoding, insideoutside
 JM 14 
10/17 
RealWorld Parsing 
lexicalization, markovization, reranking
 JM 15; Charniak 2005 
10/22 
Discriminative Methods 
tree CRF, early update perceptron
 Finkel 08, Huang 2012 
10/24 
Review 
 
10/29 
Midterm 
 
10/31 
Midterm Solutions 


11/5 
Semantics 
lambda calculus
 Zettlemoyer 2005 
11/7 
Machine Translation 
sourcechannel, model 1
 JM 25 
11/12 
Machine Translation
 model 2, 3, hmm
 Brown 1993 
11/14 
Machine Translation
 decoding
 Koehn et al. 2003 
11/19 
Machine Translation 
syntaxbased MT
 Yamada & Knight 2001 
11/21 
Machine Translation 
syntaxbased decoding
 Galley 2004, chiang 05 
11/26 
Machine Translation 
what's in a translation rule
 Och 2004 
12/3 
Machine Translation 
Parameter Optimization
 Och 2003 
12/5 
Sentiment Analysis 
 Liang 2011, Pang 2004 
12/10 
Lexical Semantics 
 JM 20, Lin 1998, Mihacea 2007 
12/12 
Review 
come to class with questions!
 
Final Exam: Fri 12/20, 8:3011:30am, CSB 703.
Grading
 Final exam: 35%
 Homeworks: 35%
 Midterm: 25%
 Class participation: 5%
 No late homework
 Programming assignments must be in python, and must run on class account
 Lecture notes must be in latex, and must compile on class account. See latex template.
 Lecture notes for Tu are due 3:25pm Th, notes for Th are due 3:25pm Mon
gildea @ cs rochester edu
December 3, 2013
