Song, Dawei; Shi, Yanjie; Zhang, Peng; Hou, Yuexian; Hu, Bin; Jia, Yuan; Huang, Qiang; Kruschwitz, Udo; De Roeck, Anne and Bruza, Peter
(2013).
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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 |
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| 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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