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NERC-fr: Supervised Named Entity Recognition for French

  • Andoni Azpeitia
  • Montse Cuadros
  • Seán Gaines
  • German Rigau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8655)

Abstract

Currently there are only few available language resources for French. Additionally there is a lack of available language models for for tasks such as Named Entity Recognition and Classification (NERC) which makes difficult building natural language processing systems for this language. This paper presents a new publicly available supervised Apache OpenNLP NERC model that has been trained and tested under a maximum entropy approach. This new model achieves state of the art results for French when compared with another systems. Finally we have also extended Apache OpenNLP libraries to support part-of-speech feature extraction component which has been used for our experiments.

Keywords

Computational Linguistics Entity Recognition Automatic Speech Recognition System Lexical Resource Sentence Boundary 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Andoni Azpeitia
    • 1
  • Montse Cuadros
    • 1
  • Seán Gaines
    • 1
  • German Rigau
    • 2
  1. 1.HSLT, IP Department - Vicomtech-IK4Donostia-San SebastiánSpain
  2. 2.IXA NLP Group - UPV/EHUDonostia-San SebastiánSpain

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