Predicting the usefulness of collection enrichment for enterprise search

Peng, J., He, B. and Ounis, I. (2009) Predicting the usefulness of collection enrichment for enterprise search. Lecture Notes in Computer Science, 5766, pp. 366-370. (doi:10.1007/978-3-642-04417-5_41)

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Publisher's URL: http://dx.doi.org/10.1007/978-3-642-04417-5_41

Abstract

Query Expansion (QE) often improves the retrieval performance of an Information Retrieval (IR) system. However, as enterprise intranets are often sparse in nature, with limited use of alternative lexical representations between authors, it can be advantageous to use Collection Enrichment (CE) to gather higher quality pseudo-feedback documents. In this paper, we propose the use of query performance predictors to selectively apply CE on a per-query basis. We thoroughly evaluate our approach on the CERC standard test collection and its corresponding topic sets from the TREC 2007 and 2008 Enterprise track document search tasks. We experiment with 3 different external resources and 3 different query performance predictors. Our experimental results demonstrate that our proposed approach leads to a significant improvement in retrieval performance.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:He, Mr Ben and Ounis, Professor Iadh and Peng, Mr Jie
Authors: Peng, J., He, B., and Ounis, I.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
ISSN:0302-9743

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