Computer Science 248/448

Statistical Speech and Language Processing

Fall 2017

Instructor: Dan Gildea (Office hours: Mon 2:00-3:00pm, Wed 1:00-2:00pm or by appointment)
TA: Linfeng Song
Location: Tu/Th 11:05am-12:20pm, CSB 601.

Homeworks

Recommended resources:


Syllabus

Onwe will coverwhich means that after class you will understandif before class you have read
8/30 Hidden Markov Models viterbi, forward JM 6
9/5 Perceptron for Tagging NLP = ML + DP Collins 2002
9/7 CRFs f - E[f] McCallum 2001
9/12 Max Margin Learning subgradients Tsochantaridis 2004, Ratliff 2006
9/14 EM for HMM JM 6
9/19 latent CRF
9/21 N-grams Good-Turing, Katz, Kneser-Ney
9/26 Context Free Grammars highest prob string, infinite trees JM 13
9/28 Context Free Grammars viterbi, inside-outside
10/3 Context Free Grammars viterbi, inside-outside
10/5 Parsing minimum risk decoding, crf parsing Finkel 08
10/10 Fall Break
10/12 Parsing em, latent crf, max-margin
10/17 Treebanks right node raising JM 12
10/19 Search beam search, A*
10/24 Dependency parsing
10/26 Review
10/31 Midterm
11/2 Midterm Solutions
11/7 Machine Translation model 1, 2, 3 Brown 1993
11/9 Machine Translation decoding Koehn et al. 2003 Galley 2004, Chiang 05
11/14 Neural Machine Translation LSTM Sutskever 2014, Bahdanau 2014
11/16 Machine Translation minimum risk training Papineni 2002, Shen 2016
11/21 Semantics word2vec Mikolov 2013, Pennington 2014
11/28 Semantics lambda calculus JM 20, Zettlemoyer 2005, Liang 2011
11/30Deep Learning neural symbolic machines Liang et al., 2017,
12/5 Deep Learning for parsing tabularization Socher 2013, Shi 2017
12/7 Something fun expected products of counts Pauls 2009
12/12 no class
Final Exam: Thursday, December 21, 4-7pm, CSB 601.

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
gildea @ cs rochester edu
December 7, 2017