BROWSE
Detail
Journal Article
Probabilistic Co-Relevance for Query-Sensitive Similarity Measurement in Information Retrieval
Cited 4 time in 
Share 
Share
- Authors
- Seung-Hoon Na
- Issue Date
- 2013-03
- Citation
- Information Processing & Management, v.49, no.2, pp.558-575
- ISSN
- 0306-4573
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- Interdocument similarities are the fundamental information source required in clusterbased retrieval, which is an advanced retrieval approach that significantly improves performance during information retrieval (IR). An effective similarity metric is query-sensitive similarity, which was introduced by Tombros and Rijsbergen as method to more directly satisfy the cluster hypothesis that forms the basis of cluster-based retrieval. Although this method is reported to be effective, existing applications of query-specific similarity are still limited to vector space models wherein there is no connection to probabilistic approaches. We suggest a probabilistic framework that defines query-sensitive similarity based on probabilistic co-relevance, where the similarity between two documents is proportional to the probability that they are both co-relevant to a specific given query. We further simplify the proposed co-relevance-based similarity by decomposing it into two separate relevance models. We then formulate all the requisite components for the proposed similarity metric in terms of scoring functions used by language modeling methods. Experimental results obtained using standard TREC test collections consistently showed that the proposed query-sensitive similarity measure performs better than term-based similarity and existing query-sensitive similarity in the context of Voorhees' nearest neighbor test (NNT). © 2012 Elsevier Ltd. All rights reserved.
- KSP Keywords
- Cluster-based retrieval, Language modeling, Modeling method, Nearest Neighbor, Probabilistic approach, Probabilistic framework, Scoring functions, Similarity Measurement, Similarity Metric, Test collections, Vector Space Model(VSM)