This paper reports about the development of a NER system in Bengali by combining outputs of the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM). The training set consists of approximately 250K wordforms and has been manually annotated with the four major named entity (NE) tags such as Person, Location, Organization and Miscellaneous tags. The classifiers make use of the different contextual information of the words along with the variety of features that are helpful in predicting the various NE classes. Lexical context patterns, which are generated from an unlabeled corpus of 1 million wordforms in a semi-automatic way, have been used as the features of the classifiers in order to improve their performance. In addition, we have used the second best tags of the classifiers and applied several heuristics to improve the performance. Finally, the classifiers are combined using a majority voting approach. Experimental results show the effectiveness of the proposed approach with the overall average recall, precision, and f-score values of 90.78%, 87.35%, and 89.03%, respectively, which shows an improvement of 11.8% in f-score over the best performing SVM based baseline system and an improvement of 15.11% in f-score over the least performing ME based baseline system. The proposed system also outperforms the other existing Bengali NER system.


Natural Language Processing Named Entity Recognition Maximum Entropy Conditional Random Field Support Vector Machine Majority Voting 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Asif Ekbal
    • 1
  • Sivaji Bandyopadhyay
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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