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Deep Learning Approach for Arabic Named Entity Recognition

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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

Inspired by recent work in Deep Learning that have achieved excellent performance on difficult problems such as computer vision and speech recognition, we introduce a simple and fast model for Arabic named entity recognition based on Deep Neural Networks (DNNs). Named Entity Recognition (NER) is the task of classifying or labelling atomic elements in the text into categories such as Person, Location or Organization. The unique characteristics and the complexity of the Arabic language make the extraction of named entities a challenging task. Most state-of-the-art systems use a combination of various Machine Learning algorithms or rely on handcrafted engineering features and the output of other NLP tasks such as part-of-speech (POS) tagging, text chunking, prefixes and suffixes as well as a large gazetteer. In this paper, we present an Arabic NER system based on DNNs that automatically learns features from data. The experimental results show that our approach outperforms the model based on Conditional Random Fields by 12.36 points in F-measure. Moreover, our model outperforms the state-of-the-art by 5.18 points in Precision and gets very close results in F-measure. Most importantly, our system can be easily extended to recognize other named entities without any additional rules or handcrafted engineering features.

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Notes

  1. 1.

    The code will be released right after the paper will be accepted.

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Authors and Affiliations

  1. Department of Computer Science, High Institute of Technology, Ibn Zohr University, Agadir, Morocco

    Mourad Gridach

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  1. Mourad Gridach
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Correspondence to Mourad Gridach .

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

    Alexander Gelbukh

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Gridach, M. (2018). Deep Learning Approach for Arabic Named Entity Recognition. 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_31

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  • DOI: https://doi.org/10.1007/978-3-319-75477-2_31

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