@inproceedings{ehsan-etal-2023-alphabrains-wojoodner,
title = "{A}lpha{B}rains at {W}ojood{NER} shared task: {A}rabic Named Entity Recognition by Using Character-based Context-Sensitive Word Representations",
author = "Ehsan, Toqeer and
Ali, Amjad and
Al-Fuqaha, Ala",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.88",
doi = "10.18653/v1/2023.arabicnlp-1.88",
pages = "783--788",
abstract = "This paper presents Arabic named entity recognition models by employing the single-task and the multi-task learning paradigms. The models have been developed using character-based contextualized Embeddings from Language Model (ELMo) in the input layers of the bidirectional long-short term memory networks. The ELMo embeddings are quite capable of learning the morphology and contextual information of the tokens in word sequences. The single-task learning models outperformed the multi-task learning models and achieved micro F1-scores of 0.8751 and 0.8884 for the flat and nested annotations, respectively.",
}
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%0 Conference Proceedings
%T AlphaBrains at WojoodNER shared task: Arabic Named Entity Recognition by Using Character-based Context-Sensitive Word Representations
%A Ehsan, Toqeer
%A Ali, Amjad
%A Al-Fuqaha, Ala
%Y Sawaf, Hassan
%Y El-Beltagy, Samhaa
%Y Zaghouani, Wajdi
%Y Magdy, Walid
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Habash, Nizar
%Y Khalifa, Salam
%Y Keleg, Amr
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y Mrini, Khalil
%Y Almatham, Rawan
%S Proceedings of ArabicNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore (Hybrid)
%F ehsan-etal-2023-alphabrains-wojoodner
%X This paper presents Arabic named entity recognition models by employing the single-task and the multi-task learning paradigms. The models have been developed using character-based contextualized Embeddings from Language Model (ELMo) in the input layers of the bidirectional long-short term memory networks. The ELMo embeddings are quite capable of learning the morphology and contextual information of the tokens in word sequences. The single-task learning models outperformed the multi-task learning models and achieved micro F1-scores of 0.8751 and 0.8884 for the flat and nested annotations, respectively.
%R 10.18653/v1/2023.arabicnlp-1.88
%U https://aclanthology.org/2023.arabicnlp-1.88
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.88
%P 783-788
Markdown (Informal)
[AlphaBrains at WojoodNER shared task: Arabic Named Entity Recognition by Using Character-based Context-Sensitive Word Representations](https://aclanthology.org/2023.arabicnlp-1.88) (Ehsan et al., ArabicNLP-WS 2023)
ACL