Boosting Arabic Named Entity Recognition Transliteration with Deep Learning

Date
2020-03-13
Authors
Journal Title
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Volume Title
Publisher
The AAAI Press
Abstract
The task of transliteration of named entities from one lan- guage into another is complicated and considered as one of the challenging tasks in machine translation (MT). To build a well performed transliteration system, we apply well-es- tablished techniques based on Hybrid Deep Learning. The system based on convolutional neural network (CNN) fol- lowed by Bi-LSTM and CRF. The proposed hybrid mecha- nism is examined on ANERCorp and Kalimat corpus. The results show that the neural machine translation approach can be employed to build efficient machine transliteration systems achieving state-ofthe-art results for Arabic - Eng- lish language.
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Citation
Alkhatib, M., & Shaalan, K. (2020). Boosting arabic named entity recognition transliteration with deep learning. In E. Bell, & R. Bartak (Eds.), Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020 (pp. 484-487). (Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020). The AAAI Press.
URI
https://bspace.buid.ac.ae/handle/1234/3066
Collections
Dr Manar Alkhatib