Tech Report

LRD: Latent Relation Discovery for Vector Space Expansion and Information Retrieval

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 effec-tively 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 rela-tionships 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.

Publication(s)

Alexandre Goncalves, Jianhan Zhu, Dawei Song, Victoria Uren, Roberto Pacheco. LRD: Latent Relation Discovery for Vector Space Expansion and Information Retrieval. In Proc. of The Seventh International Conference on Web-Age Information Management (WAIM 2006), June, Hong Kong, China.

ID: kmi-06-09

Date: 2006

Author(s): Alexandre Gonçalves, Jianhan Zhu, Dawei Song, Victoria Uren, Roberto Pacheco

Resources:
Download PDF

View By

Other Publications

Jobs

Junior Front-end and CMS Web Developer

Knowledge Media Institute (KMi)
£21,843 - £24,565
Based in Milton Keynes
Temporary contract for 12 months

The Knowledge Media Institute (KMi) within STEM is looking for a Junior Front-end & CMS Web Developer to join their team at the OU that runs a large aggregator of research papers called CORE, and a set of projects promoting principles of Open Science. CORE provides free access to the full-texts of 5 million+ Open Access research papers as well as a number of information services for researchers and organizations. These include service enabling text and data mining, recommender systems,...

CONTACT US

Knowledge Media Institute
The Open University
Walton Hall
Milton Keynes
MK7 6AA
United Kingdom

Tel: +44 (0)1908 653800

Fax: +44 (0)1908 653169

Email: KMi Support

COMMENT

If you have any comments, suggestions or general feedback regarding our website, please email us at the address below.

Email: KMi Development Team