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Term Selection for Query Expansion in Medical Cross-Lingual Information Retrieval - 2019

Term Selection For Query Expansion In Medical Cross-Lingual Information Retrieval

Research Area:  Machine Learning

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.

Keywords:  

Author(s) Name:  Shadi Saleh & Pavel Pecina

Journal name:  

Conferrence name:  European Conference on Information Retrieval

Publisher name:  Springer

DOI:  10.1007/978-3-030-15712-8_33

Volume Information: