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

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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.

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Correspondence to Ilyes Khennak.

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Ilyes Khennak is a PhD student in computer science at University of Sciences and Technology Houari Boumediene (USTHB), Algeria. He received his master degree in intelligent computer systems from USTHB in 2011. His research interests include artificial intelligence and information retrieval.

Habiba Drias received the MS degree in computer science from Case Western Reserve University, USA in 1984 and the PhD degree in computer science from University of Sciences and Technology Houari Boumediene (USTHB), Algeria in collaboration with UPMC, France in 1993. She is currently a full professor at USTHB since 1999 and directs the Laboratory of Research in Artificial Intelligence (LRIA). She has published around 200 papers in wellrecognized international conference proceedings and journals and has directed 20 PhD theses, 38 master theses and 31 engineer projects. In 2013, she won the Algerian Scopus award in computer science, and she was selected by a jury of international academicians as a founding member of the Algerian Academy of Science and Technology (AAST) in 2015.

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Khennak, I., Drias, H. Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE. Front. Comput. Sci. 12, 163–176 (2018). https://doi.org/10.1007/s11704-016-5560-0

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