Computer Science Colloquium
Tutorial on Machine Learning for Information Extraction
New York University
Friday, February 10, 2006 11:30 A.M.
Room 1302 Warren Weaver Hall
251 Mercer Street
New York, NY 10012-1185
Colloquium Information: http://cs.nyu.edu/csweb/Calendar/colloquium/index.html
I. Dan Melamed, (212) 998-3003
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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