A Semi-supervised Approach for Maximum Entropy Based Hindi Named Entity Recognition

  • Sujan Kumar Saha
  • Pabitra Mitra
  • Sudeshna Sarkar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

Scarcity of annotated data is a challenge in building high performance named entity recognition (NER) systems in resource poor languages. We use a semi-supervised approach which uses a small annotated corpus and a large raw corpus for the Hindi NER task using maximum entropy classifier. A novel statistical annotation confidence measure is proposed for the purpose. The confidence measure is used in selective sampling based semi-supervised NER. Also a prior modulation of maximum entropy classifier is used where the annotation confidence values are used as ‘prior weight’. The superiority of the proposed technique over baseline classifier is demonstrated extensively through experiments.

Keywords

Training Corpus Name Entity Recognition Entity Recognition Annotate Corpus Name Entity 
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.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sujan Kumar Saha
    • 1
  • Pabitra Mitra
    • 1
  • Sudeshna Sarkar
    • 1
  1. 1.Indian Institute of TechnologyKharagpurIndia

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