Bruza, P. D and Song, D.
|DOI (Digital Object Identifier) Link:||https://doi.org/10.1145/584792.584837|
|Google Scholar:||Look up in Google Scholar|
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.
|Item Type:||Conference Item|
|Keywords:||inference; information flow; query language modelling|
|Academic Unit/School:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Aneta Tumilowicz|
|Date Deposited:||25 Sep 2007|
|Last Modified:||29 Nov 2016 16:00|
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