Optimization of an integrated model for automatic reduction and expansion of long queries

Song, Dawei, Shi, Yanjie, Zhang, Peng, Hou, Yuexian, Hu, Bin, Jia, Yuan, Huang, Qiang, Kruschwitz, Udo, Roeck, Anne, & Bruza, Peter D. (2013) Optimization of an integrated model for automatic reduction and expansion of long queries. Lecture Notes in Computer Science : Information Retrieval Technology, 8281, pp. 133-144.

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A long query provides more useful hints for searching relevant documents, but it is likely to introduce noise which affects retrieval performance. In order to smooth such adverse effect, it is important to reduce noisy terms, introduce and boost additional relevant terms. This paper presents a comprehensive framework, called Aspect Hidden Markov Model (AHMM), which integrates query reduction and expansion, for retrieval with long queries. It optimizes the probability distribution of query terms by utilizing intra-query term dependencies as well as the relationships between query terms and words observed in relevance feedback documents. Empirical evaluation on three large-scale TREC collections demonstrates that our approach, which is automatic, achieves salient improvements over various strong baselines, and also reaches a comparable performance to a state of the art method based on user’s interactive query term reduction and expansion.

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ID Code: 68772
Item Type: Journal Article
Refereed: Yes
DOI: 10.1007/978-3-642-45068-6_12
ISBN: 978-3-642-45068-6
ISSN: 1611-3349
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Springer
Deposited On: 18 Mar 2014 22:38
Last Modified: 20 Mar 2014 00:36

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