Person Name Recognition Using the Hybrid Approach

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)


Arabic Person Name Recognition has been tackled mostly using either of two approaches: a rule-based or Machine Learning (ML) based approach, with their strengths and weaknesses. In this paper, the problem of Arabic Person Name Recognition is tackled through integrating the two approaches together in a pipelined process to create a hybrid system with the aim of enhancing the overall performance of Person Name Recognition tasks. Extensive experiments are conducted using three different ML classifiers to evaluate the overall performance of the hybrid system. The empirical results indicate that the hybrid approach outperforms both the rule-based and the ML-based approaches. Moreover, our system outperforms the state-of-the-art of Arabic Person Name Recognition in terms of accuracy when applied to ANERcorp dataset, with precision 0.949, recall 0.942 and f-measure 0.945.


Person Name Recognition Natural Language Processing Rulebased Approach Machine Learning Approach Hybrid Approach 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.The British University in DubaiUAE
  2. 2.School of InformaticsUniversity of EdinburghUK

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