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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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Authors and Affiliations
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Xiaochun Wang, Muyun Yang, Sheng Li & Tiejun Zhao
Computer Science and Technology Department, Heilongjiang Institute of Technology, China
Haoliang Qi
- Xiaochun Wang
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- Muyun Yang
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- Haoliang Qi
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- Sheng Li
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- Tiejun Zhao
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Editor information
Editors and Affiliations
School of computer Science and Technology, Tianjin University, Tianjin, 300072, China
Yuexian Hou
DIRO, University of Montreal, CP. 6128, succursale Centre-ville, H3C 3J7, Montreal, QC, Canada
Jian-Yun Nie
Institute of Software, Storage & Information Retrieval Laboratory, Chinese Academy of Sciences, 100190, Beijing, China
Le Sun
School of Computer Science and Technology, Tianjin University, 300072, Tianjin, China
Bo Wang
School of Computing, Robert Gordon University, St Andrew Street, AB25 1HG, Aberdeen, UK
Peng Zhang
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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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