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Title: LRD: latent relation discovery for vector space expansion and information retrieval.
Authors: Goncalves, Alexandre L.
Zhu, Jianhan
Song, Dawei
Uren, Victoria
Pacheco, Roberto
Editors: Yu, Jeffrey
Kitsuregawa, Masaru
Leong, Hong
Keywords: Latent relation discovery
Information retrieval
Issue Date: 2006
Publisher: Springer
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: J. YU, M. KITSUREGAWA and H. LEONG, eds. Advances in Web-Age Information Management: 7th International Conference, WAIM 2006, Hong Kong, China, June 17-19, 2006; Proceedings. Berlin: Springer. pp. 122-133.
Series/Report no.: Lecture Notes in Computer Science
4016
Abstract: In this paper, we propose a text mining method called LRD (latent relation discovery), which extends the traditional vector space model of docu-ment representation in order to improve information retrieval (IR) on docu-ments 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 rela-tion 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.
ISBN: 9783540352259
Appears in Collections:Book chapters (Computing)

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