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Term Selection for Query Expansion in Medical Cross-Lingual Information Retrieval

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

Abstract

We present a method for automatic query expansion for cross-lingual information retrieval in the medical domain. The method employs machine translation of source-language queries into a document language and linear regression to predict the retrieval performance for each translated query when expanded with a candidate term. Candidate terms (in the document language) come from multiple sources: query translation hypotheses obtained from the machine translation system, Wikipedia articles and PubMed abstracts. Query expansion is applied only when the model predicts a score for a candidate term that exceeds a tuned threshold which allows to expand queries with strongly related terms only. Our experiments are conducted using the CLEF eHealth 2013–2015 test collection and show significant improvements in both cross-lingual and monolingual settings.

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Notes

Acknowledgments

This work was supported by the Czech Science Foundation (grant n. 19-26934X).

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Formal and Applied Linguistics, Faculty of Mathematics and PhysicsCharles UniversityPragueCzech Republic

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