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An aspect query language model based on query decomposition and high-order contextual term associations

Song, Dawei, Huang, Qiang, Bruza, Peter D., & Lau, Raymond (2012) An aspect query language model based on query decomposition and high-order contextual term associations. Computational Intelligence, 28(1), pp. 1-23.

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Abstract

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

Impact and interest:

3 citations in Scopus
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2 citations in Web of Science®

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ID Code: 49102
Item Type: Journal Article
Keywords: Information retrieval , Association rules, Aspect model, Query decomposition, Document segmentation
DOI: 10.1111/j.1467-8640.2012.00407.x
ISSN: 0824-7935
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 13 Mar 2012 11:52
Last Modified: 13 Mar 2012 11:52

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