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Publication Type : Conference Paper
Publisher : IEEE
Source : 2019 International Conference on Data Science and Communication (IconDSC), IEEE, Bangalore, India (2019)
Url : https://ieeexplore.ieee.org/abstract/document/8817039
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Computer Science, Electronics and Communication
Year : 2019
Abstract : Named Entity Recognition (NER) involves finding and categorizing minute text components into pre- defined categories such as name of person, location etc. NER is a type of information extraction task which has a crucial role in improving the performance of various NLP applications. For a morphologically abundant Semitic language like Arabic, the NER task is highly challenging due to its unique morphological characteristics and peculiarities. This paper introduces a deep learning based approach for Arabic NER which make use of well-known deep neural network (DNN) architectures like Recurrent neural network (RNN), Long short term memory (LSTM), Gated recurrent unit (GRU), stacked and bidirectional versions of these three architectures. ANERcorp dataset is used for the evaluation of the Arabic NER model and Accuracy is chosen as the performance metric. On model evaluation, it is observed that bidirectional variants of DNNs provide better accuracy measures compared to their unidirectional variants.
Cite this Research Publication : K. K. Shahina, P. V. Jyothsna, G. Prabha, B. Premjith, and Dr. Soman K. P., “A Sequential Labelling Approach for the Named Entity Recognition in Arabic Language Using Deep Learning Algorithms”, in 2019 International Conference on Data Science and Communication (IconDSC), Bangalore, India, 2019.