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

  • Authors:

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

  • Affiliations:

  • Stela Institute, Florianópolis, Brazil;Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom;Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom;Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom;Stela Institute, Florianópolis, Brazil

  • Venue:
  • WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

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 between 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.