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
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
Last changed Fri, 2013-03-08 17:48

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