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

Song, D and Shi, Y and Zhang, P and Hou, Y and Hu, B and Jia, Y and Huang, Q and Kruschwitz, U and De Roeck, A and Bruza, P (2013) Optimization of an integrated model for automatic reduction and expansion of long queries. In: UNSPECIFIED, ? - ?.

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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. © 2013 Springer-Verlag.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 18 Dec 2015 17:14
Last Modified: 23 Jan 2019 01:15
URI: http://repository.essex.ac.uk/id/eprint/14724

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