A hybrid approach to Arabic named entity recognition

Khaled Shaalan, Mai Oudah

Research output: Contribution to journalArticlepeer-review


In this paper, we propose a hybrid named entity recognition (NER) approach that takes the advantages of rule-based and machine learning-based approaches in order to improve the overall system performance and overcome the knowledge elicitation bottleneck and the lack of resources for underdeveloped languages that require deep language processing, such as Arabic. The complexity of Arabic poses special challenges to researchers of Arabic NER, which is essential for both monolingual and multilingual applications. We used the hybrid approach to develop an Arabic NER system that is capable of recognizing 11 types of Arabic named entities: Person, Location, Organization, Date, Time, Price, Measurement, Percent, Phone Number, ISBN and File Name. Extensive experiments were conducted using decision trees, Support Vector Machines and logistic regression classifiers to evaluate the system performance. The empirical results indicate that the hybrid approach outperforms both the rule-based and the ML-based approaches when they are processed independently. More importantly, our system outperforms the state-of-the-art of Arabic NER in terms of accuracy when applied to ANERcorp standard dataset, with F-measures 0.94 for Person, 0.90 for Location and 0.88 for Organization.

Original languageEnglish (US)
Pages (from-to)67-87
Number of pages21
JournalJournal of Information Science
Issue number1
StatePublished - Feb 2014


  • hybrid approach
  • information extraction
  • information retrieval
  • machine learning approach
  • named entity recognition
  • natural language processing
  • rule-based approach

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences


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