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Pseudo-Relevance Feedback for Information Retrieval in Medicine Using Genetic Algorithms

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Book cover Intelligent Information and Database Systems (ACIIDS 2018)

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Abstract

Pseudo-Relevance Feedback is one of the methods for improving search engine results. By automatically extracting information from a previous search result, a new query is posed as an expansion of the original query, and then it is searched again. In this paper, we apply a genetic algorithm to improve the Pseudo-Relevance Feedback method in searching medical texts. First, a set of candidate terms is constructed by extracting keywords from the documents returned from the initial search using the original query. Then, the seed terms are selected from the candidate term set using our proposed genetic algorithm, to be merged with the original query to create a new query. The new query is searched again, returning a final ranked list of documents. Experimental results on the TREC 2014 CDS dataset show that the proposed method outperforms the baseline method that does not use a genetic algorithm for Pseudo-Relevance Feedback.

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Notes

  1. 1.

    http://eric.ed.gov.

  2. 2.

    http://www.findastronomy.com.

  3. 3.

    http://www.infotopia.info.

  4. 4.

    https://www.ncbi.nlm.nih.gov/gquery/.

  5. 5.

    https://lucene.apache.org/.

  6. 6.

    https://www.nlm.nih.gov/mesh/.

  7. 7.

    https://www.ncbi.nlm.nih.gov/pmc/.

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Acknowledgments

This work is funded by Vietnam National University at Ho Chi Minh City under the grant number B2016-42-01.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Lanh Nguyen & Tru Cao

  2. John von Neumann Institute, Vietnam National University at Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tru Cao

Authors
  1. Lanh Nguyen
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  2. Tru Cao
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Corresponding author

Correspondence to Lanh Nguyen .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Quang Binh University, Dong Hoi City, Vietnam

    Duong Hung Hoang

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Rutgers University, Piscataway, New Jersey, USA

    Hoang Pham

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Nguyen, L., Cao, T. (2018). Pseudo-Relevance Feedback for Information Retrieval in Medicine Using Genetic Algorithms. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_38

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  • DOI: https://doi.org/10.1007/978-3-319-75420-8_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75419-2

  • Online ISBN: 978-3-319-75420-8

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