Arabic Named Entity Recognition Using Variant Deep Neural Network Architectures and Combinatorial Feature Embedding Based on CNN, LSTM and BERT - ACL Anthology

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Arabic Named Entity Recognition Using Variant Deep Neural Network Architectures and Combinatorial Feature Embedding Based on CNN, LSTM and BERT

Manel Affi, Chiraz Latiri

Anthology ID:: 2022.paclic-1.33
Volume:: Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation
Month:: October
Year:: 2022
Address:: Manila, Philippines
Editors:: Shirley Dita, Arlene Trillanes, Rochelle Irene Lucas
Venue:: PACLIC
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 302–312
Language:
URL:: https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.paclic-1.33/
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Cite (ACL):: Manel Affi and Chiraz Latiri. 2022. Arabic Named Entity Recognition Using Variant Deep Neural Network Architectures and Combinatorial Feature Embedding Based on CNN, LSTM and BERT. In Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation, pages 302–312, Manila, Philippines. Association for Computational Linguistics.
Cite (Informal):: Arabic Named Entity Recognition Using Variant Deep Neural Network Architectures and Combinatorial Feature Embedding Based on CNN, LSTM and BERT (Affi & Latiri, PACLIC 2022)
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PDF:: https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.paclic-1.33.pdf

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