Instructor:Prof. Noah Smith History: Taught in Fall 2006, Fall 2007 Prerequisite: Language and Statistics (11-761) or permission of instructor.
Recommended: Algorithms for Natural Language Processing (11-711), Machine Learning (15-681, 15-781, or 11-746)
Course Description
This course covers modern empirical methods in natural language
processing. It is designed for language technologies students who want
to more deeply understand statistical methodology in the language
domain, and for machine learning students who want to know about
current problems and solutions in text processing. Students will,
upon completion, understand how statistical modeling and learning can
be applied to linguistic analysis of text, be able to develop and
apply new statistical models to problems in their own research, and be
able to critically read papers from the major related conferences. A
recurring theme will be the tradeoffs between computational cost,
mathematical elegance, expressive power, and applicability to real
problems. The course is organized around methods, with concrete
examples introduced throughout. Grading is based on a thorough written
literature review and an oral presentation of a current topic in
empirical NLP, assignments, and a final exam.
Excellent Literature Reviews
Here are some samples of excellent literature reviews completed by students who took L&S 2:
Noah A. Smith
Email:
http://www.cs.cmu.edu/~nasmith/LS2/index.html
Page last modified: 9:07PM Sunday, September 16, 2007
Document downloaded: 6:44AM Friday, May 16, 2008
