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Adaptive Weighting Approach to Context-Sensitive Retrieval Model

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Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 7675))

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Abstract

To best exploit the context information for meaningful hints to the user’s intent, this paper proposes an adaptive weighting approach to improve the current context-sensitive retrieval model. Thepotential for adaptability is first investigated as the performance gap between the current context-sensitive models with a fixed form weight and those with adaptive weights for contextual information. Then the proper context weight is predicated according to the relation strength between the query and its context. The experimental results on a public available dataset indicate that the proposed approach outperforms three baseline methods.

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References

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Xiaochun Wang, Muyun Yang, Sheng Li & Tiejun Zhao

  2. Computer Science and Technology Department, Heilongjiang Institute of Technology, China

    Haoliang Qi

Authors
  1. Xiaochun Wang

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  2. Muyun Yang

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  3. Haoliang Qi

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  4. Sheng Li

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  5. Tiejun Zhao

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Editor information

Editors and Affiliations

  1. School of computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Yuexian Hou

  2. DIRO, University of Montreal, CP. 6128, succursale Centre-ville, H3C 3J7, Montreal, QC, Canada

    Jian-Yun Nie

  3. Institute of Software, Storage & Information Retrieval Laboratory, Chinese Academy of Sciences, 100190, Beijing, China

    Le Sun

  4. School of Computer Science and Technology, Tianjin University, 300072, Tianjin, China

    Bo Wang

  5. School of Computing, Robert Gordon University, St Andrew Street, AB25 1HG, Aberdeen, UK

    Peng Zhang

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© 2012 Springer-Verlag Berlin Heidelberg

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Wang, X., Yang, M., Qi, H., Li, S., Zhao, T. (2012). Adaptive Weighting Approach to Context-Sensitive Retrieval Model. In: Hou, Y., Nie, JY., Sun, L., Wang, B., Zhang, P. (eds) Information Retrieval Technology. AIRS 2012. Lecture Notes in Computer Science, vol 7675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35341-3_37

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