Lightly-supervised attribute extraction

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 meth-ods give considerable improvements over that baseline. We also demonstrate that query expansion using extracted attributes improves retrieval performance on un-derspecified information-seeking queries. 1 Attributes in Web Search Web search engines receive numerous queries requesting information, often focused on a specific entity, such as a person, place or organization. These queries are sometimes general requests, such as “bio of George Bush, ” or specific requests, such as “new york mayor. ” Accurately identifying the entity (new york) or related attributes (mayor) can improve search results in several ways [1]. Fo