Abstract
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.
This research has been partially funded by the Spanish Government under project CICyT number TIC2003-07158-C04-01.
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Ferrández, Ó., Toral, A., Muñoz, R. (2006). Fine Tuning Features and Post-processing Rules to Improve Named Entity Recognition. In: Kop, C., Fliedl, G., Mayr, H.C., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2006. Lecture Notes in Computer Science, vol 3999. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11765448_16
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DOI: https://doi.org/10.1007/11765448_16
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