Finding good feedback documents

He, B. and Ounis, I. (2009) Finding good feedback documents. In: Conference on Information and Knowledge Management, Hong Kong, China, 2-6 Nov 2009, pp. 2011-2014. (doi:10.1145/1645953.1646289)

Full text not currently available from Enlighten.

Publisher's URL: http://dx.doi.org/10.1145/1645953.1646289

Abstract

Pseudo-relevance feedback finds useful expansion terms from a set of top-ranked documents. It is often crucial to identify those good feedback documents from which useful expansion terms can be added to the query. In this paper, we propose to detect good feedback documents by classifying all feedback documents using a variety of features such as the distribution of query terms in the feedback document, the similarity between a single feedback document and all top-ranked documents, or the proximity between the expansion terms and the original query terms in the feedback document. By doing this, query expansion is only performed using a selected set of feedback documents, which are predicted to be good among all top-ranked documents. Experimental results on standard TREC test data show that query expansion on the selected feedback documents achieves statistically significant improvements over a strong pseudo-relevance feedback mechanism, which expands the query using all top-ranked documents.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:He, Mr Ben and Ounis, Professor Iadh
Authors: 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

University Staff: Request a correction | Enlighten Editors: Update this record