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Self-supervised Relation Extraction Using UMLS

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Information Access Evaluation. Multilinguality, Multimodality, and Interaction (CLEF 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8685))

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Abstract

Self-supervised relation extraction uses a knowledge base to automatically annotate a training corpus which is then used to train a classifier. This approach has been successfully applied to different domains using a range of knowledge bases. This paper applies the approach to the biomedical domain using UMLS, a large biomedical knowledge base containing millions of concepts and relations among them. The approach is evaluated using two different techniques. The presented results are promising and indicate that UMLS is a useful resource for semi-supervised relation extraction.

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Authors and Affiliations

  1. Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, S1 4DP, Sheffield, England

    Roland Roller & Mark Stevenson

Authors
  1. Roland Roller
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  2. Mark Stevenson
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Editors and Affiliations

  1. Google Inc., Brandschenkestraße 110, 8002, Zurich, Switzerland

    Evangelos Kanoulas

  2. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstrasse 9-11, 1040, Vienna, Austria

    Mihai Lupu

  3. Information School, University of Sheffield, Sheffield, UK

    Paul Clough

  4. Department of Computer Science and IT, RMIT University, 3000, Melbourne, VIC, Australia

    Mark Sanderson

  5. Department of Computing, Edge Hill University, L39 4QP, Ormskirk, Lancashire, UK

    Mark Hall

  6. Vienna University of Technology, Austria

    Allan Hanbury

  7. Information School, University of Sheffield, Regent Court, 211 Portobello, S1 4DP, Sheffield, UK

    Elaine Toms

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Roller, R., Stevenson, M. (2014). Self-supervised Relation Extraction Using UMLS. In: Kanoulas, E., et al. Information Access Evaluation. Multilinguality, Multimodality, and Interaction. CLEF 2014. Lecture Notes in Computer Science, vol 8685. Springer, Cham. https://doi.org/10.1007/978-3-319-11382-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-11382-1_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11381-4

  • Online ISBN: 978-3-319-11382-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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