Steam-powered Turing Machine University of Washington Department of Computer Science & Engineering
CSEP 517 - Natural Language Processing - Autumn 2015
Tue 6:30-9:20 in SMI 102
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Instructor: Yejin Choi (yejin at cs dot washington dot edu)
Office hours: TBD at CSE 578 (and by appointment)
TA: Ignacio Cano (icano at cs dot washington dot edu)
Office hours: TBD at CSE 370 (and by appointment)
TA: James Ferguson (jfferg at cs dot washington dot edu)
Office hours: TBD at CSE 410-5 (and by appointment)

Schedule (subject to change)

Week Dates Topics & Lecture Slides Notes (Required) Textbook Supplementary Readings
1 Oct 6 Introduction [Slides]; Language Models (LM) [Slides] LM Notes J&M 4.1-4; M&S 6 [Large LMs] [Berkeley LM]
2 Oct 13 Sequences: Language Models and Smoothing; Hidden Markov Models (HMMs) [Slides] HMM Notes J&M 4.5-7; M&S 6 [Smoothing]
3Oct 20 Hidden Markov Models (HMMs) [Slides]& Part-Of-Speech Tagging [Slides] J&M 5.1-5.3; 6.1-6.4; M&S 9, 10.1-10.3 [TnT Tagger] [Stanford Tagger] [SOTA POS]
4 Oct 27 Trees: Probabilistic Context Free Grammars (PCFG) and Parsing [Slides] PCFG Notes, Lexicalized PCFGs J&M 13-14; M&S 11-12 [Syntax Intro] [Incremental] [Best First] [A* Parsing] [Lexicalized] [Unlexicalized] [Split Merge]
5 Nov 3 More Parsing [Slides]; Expectation Maximization (EM)[Slides] EM Notes, Forward-backward, Inside-outside J&M 6.5; M&S 9.3-4; 11.3-4 [Semi-supervised Naive Bayes] [EM Tutorial] [EM for Feature-Rich]
6 Nov 10 Machine Translation (MT): Word Alignment [Slides] IBM Models 1 and 2 J&M 25.1-6; M&S 13 [IBM Models] [HMM Model] [MERT Training]
7 Nov 17 Log-Linear / Feature-Rich Models: Conditional Random Fields (CRFs) [Slides-nov17] Log-linear models CRF Notes J&M 6.6-6.8; M&S 16.2-16.3 [MaxExt] [CRF Tutorial] [CRF LM] [CRF Parsing]
8 Nov 24 More Machine Translation (MT): Phrase-based MT [Slides]; Syntax-based MT [Slides I] [II] Phrase-based Notes J&M 25.6-10; M&S 13 [SCFG Tutorial] [Hiero] [Tree-to-String] [Tree-to-Tree]
9 Dec 1 Knowledge & Semantic Relations: Information Extraction; Entailment; [Slides] J&M 22 [Entailment Graphs] [Paraphrasing w/ MT] [Paraphrasing and Entailment]
10 Dec 8 Neural Models




We will have 4 programming-based homework assignments (80% of grade). Data/code/instruction are linked at Dropbox Please submit all your assignments to the online DropBox.

Final Mini-project

Students may replace one homework project with a final mini-project (20% of grade). While students must work individually for the homework projects, students are encouraged to work as a group for the final mini-project.


The final grade will consist of programming-based homeworks (80%), non-programming assignments (10%) and course/discussion board participation (10%). No midterm or final exam.

Course Administration and Policies

CSE logo Department of Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA  98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX