I256: Applied Natural Language Processing

I 256: Applied Natural Language Processing

Fall 2009, Barbara Rosario

Course Information

Resources

Books

We will be using the following textbooks (along with online readings and handouts)

Foundations of Statistical Natural Language Processing, Christopher Manning and Hinrich Schuetze, MIT Pess, 1999. (For the theory)
Natural Language Processing with Python, Bird, Klein & Loper, O'Reilly. (For NLP processing with Python) --NOTE: there is also a free on line version

Another NLP textbook, also used in many NLP courses:

Speech and Language Processing. Dan Jurafsky and James Martin, Prentice Hall, 2000.

This text has been around for quite a long time, but has some very useful sections. An advantage is that it is now online and free.

Natural Language Understanding, James Allan, Benjamin/Cummings Publishing Company, 1995.

Students will need to learn a bit of Python programming for this course. The first below is an excellent introduction to Python; the second is an online book.

Learning Python, Mark Lutz and David Ascher, O'Reilly, 1999.
Dive Into Python, Mark Pilgrim, 2000-2004. (Free online book.)

Software

NLTK Toolkit

NLTK Homepage, Downloads, etc.

WordNet

Orange Toolkit

A Python-based classification toolkit

Weka Toolkit

Weka Project

Many NLP Software links

Linguistics resources

Glossary of linguistic terms