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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; De Roeck, Anne and Bruza, Peter (2013). Optimization of an integrated model for automatic reduction and expansion of long queries. In: Ninth Asia Information Retrieval Societies Conference, 9-11 Dec 2013, Singapore.

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Abstract

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.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 Springer
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 38255
Depositing User: Dawei Song
Date Deposited: 23 Aug 2013 08:06
Last Modified: 07 Dec 2018 13:57
URI: http://oro.open.ac.uk/id/eprint/38255
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