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Unpublished Paper
Lightly-Supervised Attribute Extraction
  • Kedar Bellare
  • Partha Pratim Talukdar
  • Giridhar Kumaran
  • Fernando Pereira
  • Mark Liberman
  • Andrew McCallum, University of Massachusetts - Amherst
  • Mark Dredze
Web search engines can greatly benefit from knowledge about attributes of entities present in search queries. In this paper, we introduce lightly-supervised methods for extracting entity attributes from natural language text. Using these methods, we are able to extract large numbers of attributes of different entities at fairly high precision from a large natural language corpus. We compare our methods against a previously proposed pattern-based relation extractor, showing that the new methods give considerable improvements over that baseline. We also demonstrate that query expansion using extracted attributes improves retrieval performance on underspecified information-seeking queries.
Publication Date
This is the pre-published version harvested from CIIR.
Citation Information
Kedar Bellare, Partha Pratim Talukdar, Giridhar Kumaran, Fernando Pereira, et al.. "Lightly-Supervised Attribute Extraction" (2007)
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