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Arabic Named Entity Recognition Using Clustered Word Embedding

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Computational Linguistics and Intelligent Text Processing (CICLing 2018)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13396))

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Abstract

Named Entity Recognition in Arabic is a challenging topic because of morphological and lexical richness of Arabic. In this paper, we propose an Arabic NER system that is based on word embedding. Word embedding hold semantic information about the context of the words. We hypothesized that the integration of word embedding features to the conventional lexical and contextual features could improve Arabic NER performance. The Conditional Random Field (CRF) sequence classifier was used. Since most CRF implementations only support categorical features, continuous word embedding vectors are clustered. In this paper, we are mainly investigating the effect of the number of clusters on NER performance. Moreover, the combination of fine and coarse grained clusters has resulted in further recognition improvement.

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Notes

  1. 1.

    http://cs.nyu.edu/cs/faculty/grishman/muc6.html.

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Author information

Authors and Affiliations

  1. Faculty of Media Engineering and Technology, German University in Cairo, Cairo, Egypt

    Caroline Sabty, Mohamed Elmahdy & Slim Abdennadher

  2. Faculty of Informatics and Computer Science, German International University, Cairo, Egypt

    Caroline Sabty & Slim Abdennadher

Authors
  1. Caroline Sabty
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  2. Mohamed Elmahdy
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  3. Slim Abdennadher
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Corresponding author

Correspondence to Caroline Sabty .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Sabty, C., Elmahdy, M., Abdennadher, S. (2023). Arabic Named Entity Recognition Using Clustered Word Embedding. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13396. Springer, Cham. https://doi.org/10.1007/978-3-031-23793-5_4

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  • DOI: https://doi.org/10.1007/978-3-031-23793-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23792-8

  • Online ISBN: 978-3-031-23793-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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