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

Gonçalves, Alexandre; Zhu, Jianhan; Song, Dawei; Uren, Victoria; Pacheco, Roberto

Authors

Alexandre Gonçalves

Jianhan Zhu

Dawei Song

Victoria Uren

Roberto Pacheco



Contributors

Jeffrey Xu Yu
Editor

Masaru Kitsuregawa
Editor

Hong Va Leong
Editor

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.

Start Date Jun 17, 2006
Publication Date Dec 31, 2006
Publisher Springer (part of Springer Nature)
Pages 122-133
Series Title Lecture notes in computer science
Series Number 4016
ISBN 9783540352259
Institution 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
DOI https://doi.org/10.1007/11775300_11
Keywords Latent relation discovery; Information retrieval

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