CSEP 517 - Natural Language Processing

Paul G. Allen School of Computer Science & Engineering

CSEP 517 - Natural Language Processing - Autumn 2018
Tu 6:30-9:20 in JHN 175

Teaching Staff

Personnel	Contact
Instructor: Luke Zettlemoyer	lsz at cs dot washington dot edu
TA: Srini Iyer	(sviyer at cs dot washington dot edu)
TA: Mandar Joshi	(mandar90 at cs dot washington dot edu)
TA: Julian Michael	(julianjm at cs dot washington dot edu)
TA: Rowan Zellers	(rowanz at cs dot washington dot edu)

Approximate Schedule [Subject To Change]

Week	Dates	Topics & Lecture Slides	Notes (Required)	Textbook & Recommended Reading
1	Oct 2	I. Introduction [slides] II. Words: Language Models (LMs) [slides]	LM	JM 4.1-4; MS 6
2	Oct 9	II. Words: Unknown Words (Smoothing) III. Sequences: Hidden Markov Models (HMMs) [slides]	HMM	JM 4.5-7; MS 6; JM 5.1-5.3; 6.1-6.5; MS 9, 10.1-10.3
3	Oct 16	III. Sequences: Hidden Markov Models (HMMs) & EM	Forward-backward, EM	JM 6.6-6.8; JM 13-14; MS 11-12
4	Oct 23	IV. Trees: Probabilistic Context Free Grammars (PCFGs) [slides]	PCFG
5	Oct 30	IV. Trees: PCFG Grammar Refinement V. Learning (Feature-Rich Models): Log-Linear Models [slides]	Lexicalized PCFG, LogLinear	Inside-outside
6	Nov 6	V. Learning (Feature Rich Sequence Models): Maximum Entropy Markov Models (MEMMs), Conditional Random Fields (CRFs), etc. [slides]	MEMMs, CRFs
7	Nov 13	V. Learning (Feature Rich Sequence Models): continued VI. Deep Learning: Intro Neural Networks [slides]	Feed Forward NNs
8	Nov 20	VI. Deep Learning: Recurrent Neural Networks (RNNs) [slides] VII. Semantics: Distributed Semantics, Embeddings [slides]	JMv3 Vector Semantics,
9	Nov 27	VII. Semantics: Distributed Semantics, Embeddings (continued) VII. Semantics: Frame Semantics [slides]	Frame Semantics,	JM 19.4; JM 20.7
10	Dec 4	VIII. Discourse: Coreference Resolution [slides] IX. Translation: Neural MT [slides]	IBM Models 1 and 2, Phrase MT	JM 25; MS 13

Textbooks

Recommended: [JM] Dan Jurafsky and James H. Martin, Speech and Language Processing: "An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition", Prentice Hall, Second Edition, 2009. (draft chapters of the third edition available online)
Optional: [MS] Chris Manning and Hinrich Schuetze, "Foundations of Statistical Natural Language Processing", Cambridge: MIT Press, 1999 (available online, free if accessed from UW network)
Optional: [GBC] Ian GoodFellow, Yoshua Bengio, and Aaron Courville, "Deep Learning" (free online book available at deeplearningbook.org )

Assignments, Discussion Board

Available at Canvas

Grading

The grade will consist of four assignments worth 25% each (containing both written & programming problems). We will also award up to 5% extra credit for participation (in class or on discussion board).

Course Administration and Policies

Assignments must be done individually unless otherwise specified. You may discuss the subject matter with other students in the class, but all final answers must be your own work. You are expected to maintain the utmost level of academic integrity in the course.
Each assignment may be handed in up to three days late, at a penalty of 20% of the maximum grade per day. You have 5 penalty-free late day credits that you can use at any time during the quarter. The 20% substraction will apply only after you have used all your late day credits. Being late by a partial day (e.g., 1 hour) will be rounded up to 1 full day.