Fine Tuning Features and Post-processing Rules to Improve Named Entity Recognition

  • Óscar Ferrández
  • Antonio Toral
  • Rafael Muñoz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3999)


This paper presents a Named Entity Recognition (NER) system for Spanish which combines the learning and knowledge approaches. Our contribution focuses on two matters: first, a discussion about selecting the best features for a machine learning NER system. Second, an error study of this system which lead us to the creation of a set of general post-processing rules. These issues are explained in detail and then evaluated. The selection of features provides an improvement of around 2.3% over the results of our previous system while the application of the set of post-processing rules provides an increment of performance which is around 3.6%, reaching finally 83.37% f-score.


Hide Markov Model Natural Language Processing Vote Strategy Name Entity Recognition Entity Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Óscar Ferrández
    • 1
  • Antonio Toral
    • 1
  • Rafael Muñoz
    • 1
  1. 1.Natural Language Processing and Information Systems Group, Department of Software and Computing SystemsUniversity of AlicanteSpain

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