CSE 490 U - Natural Language Processing (original) (raw)
| Instructor: Yejin Choi (yejin at cs dot washington dot edu) Office hours: Wed 4:30pm - 5:30pm at CSE 578 (and by appointment) | TA: Luheng He (luheng at cs dot washington dot edu) Office hours: Tue 5pm - 5:45pm at CSE 218 TA: Maarten Sap (msap at cs dot washington dot edu) Office hours: Thu 2pm - 2:45pm at CSE 218 |
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Week
Dates
Topics & Lecture Slides
Notes (Required)
Textbook & Recommended Reading
1
Mar 28, 30, Apr 1
I. Introduction [Slides]
II. Words: Language Models (LMs) [Slides]
J&M 4.1-4; M&S 6
2
Apr 4, 6, 8
II. Words: Language Models (LMs), Smoothing [Slides]
III. Sequences: Hidden Markov Models (HMMs) [Slides]
J&M 4.5-7; M&S 6
3
Apr 11, 13, 15
III. Sequences: Hidden Markov Models (HMMs) [Slides]
III. Sequences: Part-Of-Speech Tagging (skipped) [Slides]
J&M 5.1-5.3; 6.1-6.5; M&S 9, 10.1-10.3
4
Apr 18, 20, 22
IV. Trees: Probabilistic Context Free Grammars (PCFG) [Slides]
J&M 13-14; M&S 11-12
5
Apr 25, 27, 29
IV. Trees: PCFG Grammar Refinement [Slides]
Lexicalized PCFG, Inside-outside
J&M 13-14; M&S 11-12
6
May 2, 4, 6
IV. Trees: Dependency Grammars and Mildly Context-Sensitive Grammars [Slides]
7
May 9, 11, 13
V. Semantics: Frame Semantics [Slides];
V. Semantics: Distributed Semantics, Embeddings [Slides]
J&Mv3 Vector Semantics, Dense Vectors,
Frame Semantics
J&M 19.4; J&M 20.7
8
May 16, 18, 20
VI. Learning: Log-Linear Models, Conditional Random Fields (CRFs) [Slides]
J&M 6.6 - 6.8
9
May 23, 25, 27
VI. Learning: Deep Learning[Slides]
Russell & Norvig Ch 18.7 ANNs; Bishop Ch 5 ANNs;
10
(May 30), Jun 1, Jun 3
VII. Translation: Alignment Models & Phrase-based MT [Slides]
IBM Models 1 and 2, Phrase MT, EM
J&M 25; M&S 13
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Department of Computer Science & Engineering University of Washington Box 352350 Seattle, WA 98195-2350 (206) 543-1695 voice, (206) 543-2969 FAX |