NDLI: Inferring query models by computing information flow

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Inferring query models by computing information flow

Content Provider	ACM Digital Library
Author	Bruza, P. D. Song, D.
Abstract	The language modelling approach to information retrieval can also be used to compute query models. A query model can be envisaged as an expansion of an initial query. The more prominent query models in the literature have a probabilistic basis. This paper introduces an alternative, non-probabilistic approach to query modelling whereby the strength of information flow is computed between a query Q and a term w. Information flow is a reflection of how strongly w is informationally contained within the query Q. The information flow model is based on Hyperspace Analogue to Language (HAL) vector representations, which reflects the lexical co-occurrence information of terms. Research from cognitive science has demonstrated the cognitive compatibility of HAL representations with human processing. Query models computed from TREC queries by HAL-based information flow are compared experimentally with two probabilistic query language models. Experimental results are provided showing the HAL-based information flow model be superior to query models computed via Markov chains, and seems to be as effective as a probabilistically motivated relevance model.
Starting Page	260
Ending Page	269
Page Count	10
File Format	PDF
ISBN	1581134924
DOI	10.1145/584792.584837
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2002-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Information flow Query language modelling Inference
Content Type	Text
Resource Type	Article

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