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Cross-Lingual Candidate Retrieval and Re-ranking for Biomedical Entity Linking

  • Conference paper
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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14163))

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Abstract

Biomedical entity linking is an essential building block for various clinical applications and downstream NLP tasks. However, only few annotated biomedical datasets with grounded entity mentions for non-English languages are available for training supervised machine learning models. Moreover, the majority of concept aliases in medical vocabularies are also only available in English.

In this work, we consider the problem of linking disease mentions in Spanish clinical case reports to concept identifiers in SNOMED CT, a comprehensive medical terminology system. For these concepts, only a limited number of aliases in the source language are given, but many more can be obtained from other languages and medical vocabularies. We propose a system that utilizes these multilingual aliases to retrieve candidate concepts for a given entity mention and re-ranks retrieved candidates using a trainable cross-encoder. We evaluate our system on the DisTEMIST shared task dataset of the 10th BioASQ challenge.

Our results show that supervised re-ranking outperforms the previously best-performing rule-based system, while requiring much less task-specific hyperparameter tuning. Detailed ablation experiments demonstrate that multilingual aliases are highly beneficial to improve recall during candidate generation, but hardly affect re-ranking performance.

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Acknowledgment

Parts of this work were generously supported by grants of the German Federal Ministry of Research and Education (01ZZ1802H, 01ZZ2314N) and the German Federal Ministry of Economic Affairs and Climate Action (01MJ21002A).

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Authors and Affiliations

  1. Hasso Plattner Institute for Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany

    Florian Borchert, Ignacio Llorca & Matthieu-P. Schapranow

Authors
  1. Florian Borchert
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  2. Ignacio Llorca
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  3. Matthieu-P. Schapranow
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Corresponding author

Correspondence to Florian Borchert .

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Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Avi Arampatzis

  2. University of Amsterdam, Amsterdam, The Netherlands

    Evangelos Kanoulas

  3. CERTH-ITI, Thessaloniki, Greece

    Theodora Tsikrika

  4. CERTH-ITI, Thessaloniki, Greece

    Stefanos Vrochidis

  5. Utrecht University, Utrecht, The Netherlands

    Anastasia Giachanou

  6. Elsevier, Amsterdam, The Netherlands

    Dan Li

  7. University of Amsterdam, Amsterdam, The Netherlands

    Mohammad Aliannejadi

  8. University of Lausanne, Lausanne, Switzerland

    Michalis Vlachos

  9. University of Padua, Padova, Italy

    Guglielmo Faggioli

  10. University of Padua, Padova, Italy

    Nicola Ferro

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Borchert, F., Llorca, I., Schapranow, MP. (2023). Cross-Lingual Candidate Retrieval and Re-ranking for Biomedical Entity Linking. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2023. Lecture Notes in Computer Science, vol 14163. Springer, Cham. https://doi.org/10.1007/978-3-031-42448-9_12

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  • DOI: https://doi.org/10.1007/978-3-031-42448-9_12

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