![](https://d3ilqtpdwi981i.cloudfront.net/O6GyvrB2x-bdNbB18xb12gKzxZo=/425x550/smart/https://bepress-attached-resources.s3.amazonaws.com/uploads/06/ed/1d/06ed1d4c-ecb4-48fa-aa6b-f88c7038a4ed/thumbnail_BPFile%20object.jpg)
Unpublished Paper
Lightly-Supervised Attribute Extraction
(2007)
Abstract
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.
Disciplines
Publication Date
2007
Comments
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) Available at: http://works.bepress.com/andrew_mccallum/97/