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RENA: A Named Entity Recognition System for Arabic

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Text, Speech, and Dialogue (TSD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9302))

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Abstract

The Named Entity Recognition (NER) task aims to identify and categorize proper and important nouns in a text. This Natural Language Processing task proved to be challenging for languages with a rich morphology such as the Arabic language. In this paper, We introduce a new named entity recognizer for Arabic. This recognizer is based on Conditional Random Fields (CRF) and an optimized feature set that combines contextual, lexical, morphological and gazetteers features. Our system outperforms the state-of-the-art Arabic NER systems with a F-measure of 93.5% when applied to ANERcorp standard dataset.

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Author information

Authors and Affiliations

  1. Computer, Networks, Mobility and Modeling Laboratory, FST, Hassan 1st University, Settat, Morocco

    Ismail El bazi & Nabil Laachfoubi

Authors
  1. Ismail El bazi
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  2. Nabil Laachfoubi
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Corresponding author

Correspondence to Ismail El bazi .

Editor information

Editors and Affiliations

  1. University of West Bohemia, Pilsen, Czech Republic

    Pavel Král

  2. University of West Bohemia, Pilsen, Czech Republic

    Václav Matoušek

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El bazi, I., Laachfoubi, N. (2015). RENA: A Named Entity Recognition System for Arabic. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_45

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  • DOI: https://doi.org/10.1007/978-3-319-24033-6_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24032-9

  • Online ISBN: 978-3-319-24033-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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