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| About site: Artificial Intelligence/Neural Networks/People - McCallum, Andrew |
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| About site: http://www.cs.umass.edu/~mccallum/ |
Title: Artificial Intelligence/Neural Networks/People - McCallum, Andrew Machine learning, text and information retrieval and extraction, reinforcement learning.
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Andrew McCallum's Home Page
Andrew McCallum
Contact Info
Bio & Affiliations
Vita
Teaching
Publications
Research & Projects
Code & Data
Students & other collab's
Activities & Events
Personal
Links:
UMass ML Seminar
Andrew McCallum
Associate Professor
Department of Computer Science
University of Massachusetts Amherst
140 Governors Drive
Amherst, MA 01003
voice: (413) 545-1323
fax: (413) 545-1789
mccallum@cs.umass.edu
Research
The main goal of my research is to dramatically increase our ability to mine actionable knowledge from unstructured text. I am especially interested in information extraction from the Web, understanding the connections between people and between organizations, expert finding, social network analysis, and mining the scientific literature & community. Toward this end my group develops and employs various methods in statistical machine learning, natural language processing, information retrieval and data mining---tending toward probabilistic approaches and graphical models. For more information see our current projects and publications.
Teaching
This coming Fall I'm co-teaching Our Networked World, with David Jensen.
News & Notables
An analysis of topical trends in the past five years of ICML.
With Sam Roweis, am I Co-Program-Chair of ICML 2008.
Generalized Expectation is an accurate way to train models by labeling features.
We have publicly launched Rexa, a new research paper search engine. It is a sibling to CiteSeer and Google Scholar, except that it provides search and browsing over more "object types", including not just papers, but also people, grants and topics.
Charles Sutton and I have a comprehensive introduction to conditional random fields, a book chapter in Lise Getoor and Ben Taskar's book on statistical relational learning.
I've writen an introduction to information extraction by machine learning, intended for an audience that doesn't know machine learning. Information Extraction: Distilling Structured Data from Unstructured Text . Andrew McCallum. ACM Queue, Volume 3, Number 9, November 2005.
MALLET is a Java toolkit for machine learning applied to natural language. It provides facilities for document classification, information extraction, part-of-speech tagging, noun phrase segmentation, general finite state transducers and classification, and much more---all desgined to be extremely efficient for large data and feature sets. Although quite mature in functionality, documentation is still sparse.
Three of my papers made it into CiteSeer's list of most cited computer science papers.
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Machine | learning, | text | and | information | retrieval | and | extraction, | reinforcement | learning. |
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http://www.cs.umass.edu/~mccallum/
McCallum, Andrew 2008 October
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Machine learning, text and information retrieval and extraction, reinforcement learning.
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