CS 224N / Ling 237 -- Natural Language Processing

CS 224N -- Ling 237
Natural Language Processing
Spring ~~2003~~ 2004

Course Information

Lecture: 4/3 units, MW 1:15-2:30 Gates B08
Section: 0 units, TBA

Note: everything below relates to the course as taught in 2003. The course in 2004 will be similar, but with a little more emphasis on language and deeper processing. A new syllabus will be posted by Wednesday. You can also collect it in class.

Announcements

[Apr 13, 2003] Common doubts on the homeworks are answered on the FAQs page

[Apr 16, 2003] You can find links to past projects here: 2000 2001 2002

[May 27, 2003] An (improved) version of the programming project 2 results is here

Useful Information and Handouts

Course Description

This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Word-level, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.

Prerequisites

Adequate experience with programming and formal structures (e.g., CS106 and CS103X).
Programming projects will be written in Java, so knowledge of Java (or a willingness to learn on your own) is required.
Knowledge of standard concepts in artificial intelligence and/or computational linguistics (e.g., CS121/221 or Ling 138/238).
Basic familiarity with logic, vector spaces, and probability.

Intended Audience

Graduate students and advanced undergraduates specializing in computer science, linguistics, or symbolic systems.

Section

Sections will be held most weeks to go over background material, or to work through problems of the sort found in written and programming assignments. Students are strongly encouraged to attend sections for a better understanding of background material and the assignments.

Required Materials

Textbook and Readings

The required text is

Christopher Manning and Hinrich Schütze, Foundations of Statistical Natural Language Processing. MIT Press, 1999. ($60 in the bookstore.)

Please see http://nlp.stanford.edu/fsnlp/ for supplementary information about the text, including errata, and pointers to online resources.

Additional useful reference texts for NLP are:

James Allen. 1995. Natural Language Understanding. Benjamin/Cummings, 2ed.
Gerald Gazdar and Chris Mellish. 1989. Natural Language Processing in X. Addison-Wesley.
Dan Jurafsky and James Martin. 2000. Speech and Language Processing. Prentice Hall.

Additional papers will occaisionally be distributed and discussed during the course of the class.

Copies of in-class hand-outs, such as homework assignments and problem set solutions, will be posted here, and hard copies will also be available outside Gates 419 (in front of Prof Manning's room) while supplies last.

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