CIS 639
Introduction
to Statistical Natural Language Processing
Spring 2000
Moore 556
898-2538
Prerequisites: The course will assume
familiarity with Natural Language Processing, elementary statistics, and simple
programming. CSE 530 as taught in Fall
1999 by Steve Bird is a perfect introduction.
This
course is intended to provide a fairly broad but thorough introduction to
Statistical NLP sufficient to allow independent reading and understanding of
the current research literature and to allow the execution of
intermediate-level research projects in Statistical NLP. The syllabus will roughly follow the Manning
and Schütze text.
Syllabus:
·
A
brief review of discrete probability theory, information theory and Unix tools
for text manipulation
·
Statistical
tools for investigating the structure of text:
Collocations and n-grams.
·
Word-sense
disambiguation
·
Part
of speech tagging: Markov models, Brill
learners, etc.
·
Probabilistic
Parsing: NP chunking, PCFGs, skeletal
grammars, statistical TAG parsing,
·
Statistical
Machine Translation
·
Information
Retrieval
·
Current
hot topics: Combining information
sources, boosting and the like, Multilingual IR
Text:
Chris
Manning and Hinrich Schütze, Foundations of Statistical Natural Language
Processing, MIT Press
Additional Texts:
Fredrick
Jelinek, Statistical Methods for Speech Recognition, MIT Press. (An
encyclopedic, quite advanced introduction)
B.
V. Gnedenko and A. Ya. Khinchin, An Elementary Introduction to the Theory of
Probability, Dover Publications. (A very good simple introduction to discrete
probability for those with little mathematical background)
David
Yarowsky, Three Machine Learning
Algorithms for Lexical Ambiguity Resolution, Ph.D. Dissertation, U. of
Pennsylvania, 1996
Michael
Collins, Head-Driven Statistical Models
for Natural Language Parsing, Ph.D. dissertation, U. of Pennsylvania, 1999
Various papers, to be distributed.