Computer Science Colloquium

Tutorial on Machine Learning for Information Extraction

Heng Ji
New York University

Friday, February 10, 2006 11:30 A.M.
Room 1302 Warren Weaver Hall
251 Mercer Street
New York, NY 10012-1185

Directions: http://cs.nyu.edu/csweb/Location/directions.html
Colloquium Information: http://cs.nyu.edu/csweb/Calendar/colloquium/index.html

Hosts:

I. Dan Melamed, (212) 998-3003

Abstract

In this tutorial, we will present a general introduction of basic machine learning techniques and their applications in Natural Language Processing. We will introduce three different methods: Hidden Markov Model, Maximum Entropy Model and K-Nearest Neighbor. Every method has a suitable problem. We will illustrate them by providing a quick glance at several Information Extraction components such as Mention Detection, Reference Resolution and Relation Detection.


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