Integrating Rule-Based System with Classification for Arabic Named Entity Recognition

  • Sherief Abdallah
  • Khaled Shaalan
  • Muhammad Shoaib
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7181)


Named Entity Recognition (NER) is a subtask of information extraction that seeks to recognize and classify named entities in unstructured text into predefined categories such as the names of persons, organizations, locations, etc. The majority of researchers used machine learning, while few researchers used handcrafted rules to solve the NER problem. We focus here on NER for the Arabic language (NERA), an important language with its own distinct challenges. This paper proposes a simple method for integrating machine learning with rule-based systems and implement this proposal using the state-of-the-art rule-based system for NERA. Experimental evaluation shows that our integrated approach increases the F-measure by 8 to 14% when compared to the original (pure) rule based system and the (pure) machine learning approach, and the improvement is statistically significant for different datasets. More importantly, our system outperforms the state-of-the-art machine-learning system in NERA over a benchmark dataset.


Name Entity Recognition Computational Linguistics Entity Recognition Arabic Language 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sherief Abdallah
    • 1
    • 2
  • Khaled Shaalan
    • 1
    • 2
  • Muhammad Shoaib
    • 2
  1. 1.University of EdinburghUK
  2. 2.British University in DubaiUAE

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