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Enhancing the Set-Based Model Using Proximity Information

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String Processing and Information Retrieval (SPIRE 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2476))

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Abstract

(SBM), which is an effective technique for computing term weights based on co-occurrence patterns, employing the information about the proximity among query terms in documents. The intuition that semantically related term occurrences often occur closer to each other is taken into consideration, leading to a new information retrieval model called proximity set-based model (PSBM). The novelty is that the proximity information is used as a pruning strategy to determine only related co-occurrence term patterns. This technique is time efficient and yet yields nice improvements in retrieval effectiveness. Experimental results show that PSBM improves the average precision of the answer set for all four collections evaluated. For the CFC collection, PSBM leads to a gain relative to the standard vector space model (VSM), of 23% in average precision values and 55% in average precision for the top 10 documents. PSBM is also competitive in terms of computational performance, reducing the execution time of the SBM in 21% for the CISI collection.

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Author information

Authors and Affiliations

  1. Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, 30161-970, Belo Horizonte-MG, Brazil

    Bruno Pôssas, Nivio Ziviani & Wagner Meira Jr.

Authors
  1. Bruno Pôssas
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  2. Nivio Ziviani
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  3. Wagner Meira Jr.
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Editor information

Editors and Affiliations

  1. Departamento de Ciěncia da Computação, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG, Brazil

    Alberto H. F. Laender

  2. Instituto Superior Técnico, INESC-ID, R. Alves Redol 9, 1000-029, Lisboa, Portugal

    Arlindo L. Oliveira

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© 2002 Springer-Verlag Berlin Heidelberg

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Pôssas, B., Ziviani, N., Meira, W. (2002). Enhancing the Set-Based Model Using Proximity Information. In: Laender, A.H.F., Oliveira, A.L. (eds) String Processing and Information Retrieval. SPIRE 2002. Lecture Notes in Computer Science, vol 2476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45735-6_11

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  • DOI: https://doi.org/10.1007/3-540-45735-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44158-8

  • Online ISBN: 978-3-540-45735-0

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