Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE

doi:10.1007/s11704-016-5560-0

Frontiers of Computer Science

2018, Vol. 12

Issue (1): 163-176 https://doi.org/10.1007/s11704-016-5560-0

本期目录

Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE

Ilyes KHENNAK(

), Habiba DRIAS

Laboratory for Research in Artificial Intelligence, Computer Science Department, University of Sciences and Technology Houari Boumediene (USTHB), Algiers 16111, Algeria

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Abstract：

Because of users’ growing utilization of unclear and imprecise keywords when characterizing their information need, it has become necessary to expand their original search queries with additional words that best capture their actual intent. The selection of the terms that are suitable for use as additional words is in general dependent on the degree of relatedness between each candidate expansion term and the query keywords. In this paper, we propose two criteria for evaluating the degree of relatedness between a candidate expansion word and the query keywords: (1) co-occurrence frequency, where more importance is attributed to terms occurring in the largest possible number of documents where the query keywords appear; (2) proximity, where more importance is assigned to terms having a short distance from the query terms within documents. We also employ the strength Pareto fitness assignment in order to satisfy both criteria simultaneously. The results of our numerical experiments on MEDLINE, the online medical information database, show that the proposed approach significantly enhances the retrieval performance as compared to the baseline.

Key words： information retrieval query expansion pseudorelevance feedback proximity multi-objective optimization Pareto dominance MEDLINE

收稿日期: 2015-12-26 出版日期: 2018-01-12

Corresponding Author(s): Ilyes KHENNAK

引用本文:

. [J]. Frontiers of Computer Science, 2018, 12(1): 163-176.
Ilyes KHENNAK, Habiba DRIAS. Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE. Front. Comput. Sci., 2018, 12(1): 163-176.

链接本文:

https://academic.hep.com.cn/fcs/CN/10.1007/s11704-016-5560-0
https://academic.hep.com.cn/fcs/CN/Y2018/V12/I1/163

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