||CS 224N -- Ling 237
Natural Language Processing
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
[Apr 13, 2003] Common doubts on the homeworks are answered on the
[Apr 16, 2003] You can find links to past projects here:
[May 27, 2003]
An (improved) version of the programming project 2 results is
Useful Information and Handouts
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.
Adequate experience with programming and formal structures (e.g., CS106
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.
Graduate students and advanced undergraduates specializing in computer
science, linguistics, or symbolic systems.
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.
Textbook and Readings
The required text is
Please see http://nlp.stanford.edu/fsnlp/
for supplementary information about the text, including errata, and pointers
to online resources.
Christopher Manning and Hinrich Schütze, Foundations
of Statistical Natural Language Processing. MIT Press, 1999. ($60
in the bookstore.)
Additional useful reference texts for NLP are:
Additional papers will occaisionally be distributed and discussed during
the course of the class.
- 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.
Copies of in-class hand-outs, such as homework assignments and problem
set solutions, will be posted here, and
copies will also be available outside Gates 419 (in front of Prof Manning's room) while
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