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Machine Reading

We seek to apply natural language, information extraction and machine learning methods to build semantic representations of individual texts and large corpora such as the WWW.

Demos

  • Open Information Extraction
  • Twitter Status Calendar
  • Review Miner

People

  • Pedro Domingos
  • Oren Etzioni
  • Anthony J Fader
  • Justin Huang
  • Jeff Huang
  • Chloe M Kiddon
  • Xiao Ling
  • Mausam
  • Mathias Niepert
  • Alan Ritter
  • Daniel S. Weld
  • Luke Zettlemoyer
  • Congle Zhang

Publications

  • Fine-Grained Entity Recognition (2012)
  • Ontological Smoothing for Relation Extraction with Minimal Supervision (2012)
  • Personalized Online Education — A Crowdsourcing Challenge (2012)
  • Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models (2011)
  • Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations (2011)
  • Approximation by Quantization (2011)
  • Probabilistic Theorem Proving (2011)
  • Sum-Product Networks: A New Deep Architecture (2011)
  • Learning First-Order Horn Clauses from Web Text (2010)
  • Learning 5000 Relational Extractors (2010)
  • Open Information Extraction using Wikipedia (2010)
  • Temporal Information Extraction (2010)
  • Amplifying Community Content Creation with Mixed Initiative Information Extraction (2009)
  • Temporal-Informatics of the WWW (2009)

Research Groups

  • Artificial Intelligence
  • Machine Learning
Last changed Fri, 2013-03-08 17:48
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Computer Science & Engineering   University of Washington   Box 352350   Seattle, WA 98195-2350   (206) 543-1695 voice, (206) 543-2969 FAX   

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