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Improving Phenotype Name Recognition

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Advances in Artificial Intelligence (Canadian AI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6657))

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Abstract

Due to the rapidly increasing amount of biomedical literature, automatic processing of biomedical papers is extremely important. Named Entity Recognition (NER) in this type of writing has several difficulties. In this paper we present a system to find phenotype names in biomedical literature. The system is based on Metamap and makes use of the UMLS Metathesaurus and the Human Phenotype Ontology. From an initial basic system that uses only these preexisting tools, five rules that capture stylistic and linguistic properties of this type of literature are proposed to enhance the performance of our NER tool. The tool is tested on a small corpus and the results (precision 97.6% and recall 88.3%) demonstrate its performance.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Western Ontario, London, ON, Canada

    Maryam Khordad, Robert E. Mercer & Peter Rogan

  2. Department of Biochemistry, The University of Western Ontario, London, ON, Canada

    Peter Rogan

Authors
  1. Maryam Khordad
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  2. Robert E. Mercer
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  3. Peter Rogan
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, 3737 Wascana Parkway, Regina, S4S 0A2, Saskatchewan, Canada

    Cory Butz

  2. Department of Mathematics and Computing Science, Saint Mary’s University, B3H 3C3, Halifax, Nova Scotia, Canada

    Pawan Lingras

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© 2011 Springer-Verlag Berlin Heidelberg

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Khordad, M., Mercer, R.E., Rogan, P. (2011). Improving Phenotype Name Recognition. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_30

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  • DOI: https://doi.org/10.1007/978-3-642-21043-3_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21042-6

  • Online ISBN: 978-3-642-21043-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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