• DocumentCode
    1680613
  • Title

    A Genetic Approach for Biomedical Named Entity Recognition

  • Author

    Ekbal, Asif ; Saha, Sriparna ; Sikdar, Utpal Kumar ; Hasanuzzaman, Md

  • Author_Institution
    Univ. of Trento, Trento, Italy
  • Volume
    2
  • fYear
    2010
  • Firstpage
    354
  • Lastpage
    355
  • Abstract
    In this paper, we report a classifier ensemble technique using the search capability of genetic algorithm (GA) for Named Entity Recognition (NER) in biomedical domain. We use Maximum Entropy (ME) framework to build a number of classifiers depending upon the various representations of a set of features. The proposed technique is evaluated with the JNLPBA 2004 data sets that yield the overall recall, precision and F-measure values of 67.98%, 71.68% and 69.78%, respectively.
  • Keywords
    biology computing; genetic algorithms; maximum entropy methods; pattern classification; F-measure value; biomedical domain; biomedical named entity recognition; classifier ensemble; data set; genetic algorithm; maximum entropy framework; search capability; Biological cells; Context; Entropy; Gallium; Genetic algorithms; Genetics; Training data; Biomedical named entity recognition; Genetic algorithm; Maximum Entropy;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on
  • Conference_Location
    Arras
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4244-8817-9
  • Type

    conf

  • DOI
    10.1109/ICTAI.2010.125
  • Filename
    5670082
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1680613