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Automatic Term Identification by User Profile for Document Categorisation in Medline

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Natural Language Processing and Information Systems (NLDB 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6716))

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Abstract

We show how term extraction methods such as AMTE X and MMT X can be used for the automatic categorisation of medical documents by user profile (novice users and experts). This is achieved by mapping document terms to external lexical resources such as WordNet, and MeSH (the medical thesaurus of NLM).

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References

  1. Hliaoutakis, A., Zervanou, K., Petrakis, E.: The AMTE X Approach in the Medical Document Indexing and Retrieval Application. Data and Knowledge Engineering 68(3), 380–392 (2009)

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  2. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison Wesley, Longman (1999)

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Author information

Authors and Affiliations

  1. Intelligent Systems Laboratory, Electronic and Computer Engineering Dept., Technical University of Crete (TUC), Chania, Crete, Greece

    Angelos Hliaoutakis & Euripides G. M. Petrakis

Authors
  1. Angelos Hliaoutakis
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  2. Euripides G. M. Petrakis
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Editor information

Editors and Affiliations

  1. School of Computing, University of Alicante, 03080, Alicante, Spain

    Rafael Muñoz

  2. Department of Software and Computing Systems, University of Alicante, Aptdo. de Correos 99, 03080, Alicante, Spain

    Andrés Montoyo

  3. CNAM- Laboratoire Cédric, 292 Rue St. Martin, 75141, Paris Cedex 03, France

    Elisabeth Métais

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© 2011 Springer-Verlag Berlin Heidelberg

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Hliaoutakis, A., Petrakis, E.G.M. (2011). Automatic Term Identification by User Profile for Document Categorisation in Medline. In: Muñoz, R., Montoyo, A., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2011. Lecture Notes in Computer Science, vol 6716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22327-3_26

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  • DOI: https://doi.org/10.1007/978-3-642-22327-3_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22326-6

  • Online ISBN: 978-3-642-22327-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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