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

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.

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