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Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition

Author

Listed:
  • Mohammed N. A. Ali

    (School of Information Science and Engineering, Central South University, Changsha 410083, China)

  • Guanzheng Tan

    (School of Information Science and Engineering, Central South University, Changsha 410083, China)

  • Aamir Hussain

    (Department of Computer Science, Muhammad Nawaz Shareef University of Agriculture, Multan 60000, Pakistan)

Abstract

Recurrent neural network (RNN) has achieved remarkable success in sequence labeling tasks with memory requirement. RNN can remember previous information of a sequence and can thus be used to solve natural language processing (NLP) tasks. Named entity recognition (NER) is a common task of NLP and can be considered a classification problem. We propose a bidirectional long short-term memory (LSTM) model for this entity recognition task of the Arabic text. The LSTM network can process sequences and relate to each part of it, which makes it useful for the NER task. Moreover, we use pre-trained word embedding to train the inputs that are fed into the LSTM network. The proposed model is evaluated on a popular dataset called “ANERcorp.” Experimental results show that the model with word embedding achieves a high F-score measure of approximately 88.01%.

Suggested Citation

  • Mohammed N. A. Ali & Guanzheng Tan & Aamir Hussain, 2018. "Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition," Future Internet, MDPI, vol. 10(12), pages 1-12, December.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:12:p:123-:d:190190
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    References listed on IDEAS

    as
    1. Khaled Shaalan & Hafsa Raza, 2009. "NERA: Named Entity Recognition for Arabic," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1652-1663, August.
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