LRD: latent relation discovery for vector space expansion and information retrieval.

Abstract

In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of document representation in order to improve information retrieval (IR) on documents and document clustering. Our LRD method extracts terms and entities, such as person, organization, or project names, and discovers relationships be-tween them by taking into account their co-occurrence in textual corpora. Given a target entity, LRD discovers other entities closely related to the target effectively and efficiently. With respect to such relatedness, a measure of relation strength between entities is defined. LRD uses relation strength to enhance the vector space model, and uses the enhanced vector space model for query based IR on documents and clustering documents in order to discover complex relationships among terms and entities. Our experiments on a standard dataset for query based IR shows that our LRD method performed significantly better than traditional vector space model and other five standard statistical methods for vector expansion.

Citation

GONCALVES, A., ZHU, J., SONG, D., UREN, V. and PACHECO, R. 2006. LRD: latent relation discovery for vector space expansion and information retrieval. In Yu, J.X., Kitsuregawa, M. and Leong, H.V. (eds.) Advances in web-age information management: proceedings of the 7th International conference on web-age information management (WAIM 2006), 17-19 June 2006, Hong Kong, China. Lecture notes in computer science, 4016. Berlin: Springer [online], pages 122-133. Available from: https://doi.org/10.1007/11775300_11

• Conference Name: 7th International conference on web-age information management (WAIM 2006)
• Conference Location: Hong Kong, China
• Start Date: Jun 17, 2006
• End Date: Jun 19, 2006
• Acceptance Date: Dec 31, 2006
• Online Publication Date: Dec 31, 2006
• Publication Date: Dec 31, 2006
• Deposit Date: May 29, 2009
• Publicly Available Date: May 29, 2009
• Publisher: Springer
• Pages: 122-133
• Series Title: Lecture notes in computer science
• Series Number: 4016
• ISBN: 3540352252; 9783540352259
• DOI: https://doi.org/10.1007/11775300_11
• Keywords: Latent relation discovery; Information retrieval
• Public URL: http://hdl.handle.net/10059/350