CS 159 - Natural Language Processing

Department of Computer Science
Pomona College CS 159 - Natural Language Processing
Spring 2011

An introduction to fundamental concepts in natural language processing and computational linguistics, a field whose goals include enabling human-computer interaction via language and text translation and understanding. Use of statistical and logical techniques to study language processing at word, synatactic, and semantic levels. Assignments require constructing and modifying systems as well as using large, existing corpora for validation.

instructor: Dave Kauchak
e-mail: [first_initial][last_name]@cs.pomona.edu
office hours: Edmunds 221
Mon/Wed 2:30-4pm, Tue/Th 10am-11
and by appointment

time: M/W 1:15-2:30pm
location: Edmunds 114
web page: http://www.cs.pomona.edu/classes/cs159/

textbook:

SPEECH and LANGUAGE PROCESSING: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (Second Edition). 2009. Daniel Jurafsky and James H. Martin. Prentice Hall, New Jersey.
*** Make sure you get the second edition since a lot of material has changed from the first edition.

assignment submission: How to submit assignments

administrivia

Announcements

Here is a handout walking through an absolute discounting smoothing example.
To clarify the SSN question part ii., the first three digits of the number must be less than or equal to 772. The remaining 6, can be any numbers.

Schedule

Note: This is a very tentative schedule and is very likely to change

Date	Topic	Reading	Assignment	Comments
1/19	introduction (ppt)	Ch. 1, article	Assignment 0
1/24	corpus analysis, regex (ppt)	Ch. 2.1	Assignment 1
1/26	regex, probability (ppt)	Ch. 3.1
1/31	probability	Ch. 3.8-3.13		Coin tossing papers 1, 2 (for those that are curious)
2/2	language modeling (ppt)	Ch. 4.1-4.4
2/7	language modeling (ppt) discounting example	Ch. 4.5-4.12 article	Assignment 2
2/9	POS, CFGs (ppt)	Ch. 5-5.3, 12.1, 12.2
2/14	CFGs, parsing (ppt)	Ch. 13-13.4		Quiz 1
2/16	parsing, PCFGs (ppt)	Ch. 14-14.4	Assignment 3
2/21	parsing: better models (ppt)	Ch. 14.5-14.11 article
2/23	EM (ppt)
2/28	regression (ppt)	Ch. 6.6	Assignment 4
3/2	maximum entropy (ppt)	Ch. 6.7 article
3/7	ML lab Quiz #2 topics
3/9	CKY work			Quiz 2
3/14	Spring break
3/16	Spring break
3/21	Text similarity (ppt)	Ch. 20.6-20.8	Assignment 5
3/23	Word similarity (ppt)		Final project
3/28	information retrieval (ppt)	IR CH. 1
3/30	student pres (IR) 1. Scott, Maksym (ppt) 2. Devin, Dandre	Article 1 (on campus) Article 2
4/4	machine translation (ppt)
4/6	machine translation	Ch. 25-25.4		Quiz 3
4/11	student pres (MT) 1. Jonny, Chrysanthia, Daniel M. (ppt) 2. Eric, Benson	Article 1 Article 2 Old Article 2
4/13	information extraction (ppt)	Ch. 22-22.2
4/18	student pres (IE) 1. Kathryn, Audrey 2. Josh, Michael	Article 1 Article 2
4/20	question answering/summarization (ppt)	Ch. 23.2-23.7
4/25	student pres (QA) 1. Dustin, Brennen 2. Sam, Martin (ppt)	Article 1 Article 2
4/27	student pres (summarization) 1. Andres, Camille (ppt) 2. Jeremy, Dan F. (ppt)	Article 1 Article 2
5/2	Project presentations
5/4	Project presentations			Quiz 4

We will do a lab in our final exam slot on Tuesday, May 10 at 2:00pm.