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Arabic Pronominal Anaphora Resolution Based on New Set of Features

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9623))

Abstract

In this paper, we present a machine learning approach for Arabic pronominal anaphora resolution. This approach resolves anaphoric pronouns without using linguistic or domain knowledge, nor deep parsing. It relies on some features which are widely used in the literary for other languages such as English. In addition, we propose new features specific for Arabic language. We provide a practical implementation of this approach which has been evaluated on three data sets (a technical manual, newspaper articles and educational texts). The results of evaluation shows that our approach provide good performance for resolving the Arabic pronominal anaphora. The measures of F-measure are respectively 86.2% for the genre of technical manuals, 84.5% for newspaper articles and 72.1% for the literary texts.

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Notes

  1. 1.

    Weka: http://weka.wikispaces.com/.

  2. 2.

    The parameters for these classifiers are those of Weka’s default settings.

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Author information

Authors and Affiliations

  1. ANLP Research Group-MIRACL Laboratory, Faculty of Economic Sciences and Management, B.P. 1088, 3018, Sfax, Tunisia

    Souha Mezghani Hammami & Lamia Hadrich Belguith

Authors
  1. Souha Mezghani Hammami
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  2. Lamia Hadrich Belguith
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Corresponding author

Correspondence to Souha Mezghani Hammami .

Editor information

Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Hammami, S.M., Belguith, L.H. (2018). Arabic Pronominal Anaphora Resolution Based on New Set of Features. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_38

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  • DOI: https://doi.org/10.1007/978-3-319-75477-2_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75476-5

  • Online ISBN: 978-3-319-75477-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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