Document (#33005)

Author

Lee, C.
Lee, G.G.

Title

Probabilistic information retrieval model for a dependence structured indexing system

Source

Information processing and management. 41(2005) no.2, S.161-176

Year

2005

Abstract

Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each other. However, conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence into a probabilistic retrieval model by adapting a dependency structured indexing system using a dependency parse tree and Chow Expansion to compensate the weakness of the assumption. In this paper, we describe a theoretic process to apply the Chow Expansion to the general probabilistic models and the state-of-the-art 2-Poisson model. Through experiments on document collections in English and Korean, we demonstrate that the incorporation of term dependences using Chow Expansion contributes to the improvement of performance in probabilistic IR systems.

Theme

Retrievalalgorithmen

Similar documents (content)

1. Cho, B.-H.; Lee, C.; Lee, G.G.: Exploring term dependences in probabilistic information retrieval model (2003) 1.99

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2. Losee, R.M.: Term dependence : truncating the Bahadur Lazarsfeld expansion (1994) 0.25

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3. Bruza, P.D.; Huibers, T.W.C.: ¬A study of aboutness in information retrieval (1996) 0.22

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4. Lassalle, E.; Lassalle, E.: Semantic models in information retrieval (2012) 0.21

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5. Larkey, L.S.; Connell, M.E.: Structured queries, language modelling, and relevance modelling in cross-language information retrieval (2005) 0.21

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