IBM Watson is a computer system capable of answering rich natural language questions and estimating its confidence in those answers at a level of the best humans at the task. On Feb 14-16, in an televised event, Watson triumphed over the best human players of all time on the American quiz show, Jeopardy!. In this course we will discuss the main principles of natural language processing, computer representation of knowledge and discuss how Watson solved some of its answers (right and wrong).

Lectures: 2 sessions / week, 1 hour and 20 min. / session:
TuTh 9:50am-11:10am, Computer Science room 2114

Students should have some experience in a programming language. No background in NLP or KR is necessary.

At the end of this class you should be able to understand current papers in the field of Natural Language Processing (NLP).

We will use chapters from multiple books (no textbook is required):
Jurafsky, D. and Martin, J. H. Speech and Language Processing.
Prentice Hall: 2000. ISBN: 0130950696
Manning, C. D. and H. Schütze: Foundations of Statistical Natural Language Processing. The MIT Press. 1999. ISBN 0-262-13360-1.

See current course webpage for more information.

See current course webpage for more information.