Chemical Named Entity Recognition: Improving Recall Using a Comprehensive List of Lexical Features

  • Andre Lamurias
  • João Ferreira
  • Francisco M. Couto
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 294)

Abstract

As the number of published scientific papers grows everyday, there is also an increasing necessity for automated named entity recognition (NER) systems capable of identifying relevant entities mentioned in a given text, such as chemical entities. Since high precision values are crucial to deliver useful results, we developed a NER method, Identifying Chemical Entities (ICE), which was tuned for precision. Thus, ICE achieved the second highest precision value in the BioCreative IV CHEMDNER task, but with significant low recall values. However, this paper shows how the use of simple lexical features was able to improve the recall of ICE while maintaining high levels of precision. Using a selection of the best features tested, ICE obtained a best recall of 27.2% for a precision of 92.4%.

Keywords

Text mining Conditional Random Fields Named Entity Recognition Chemical Compounds ChEBI 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andre Lamurias
    • 1
  • João Ferreira
    • 1
  • Francisco M. Couto
    • 1
  1. 1.Dep. de Informática, Faculdade de CiênciasUniversidade de LisboaLisboaPortugal

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