@inproceedings{hussein-etal-2023-alex,
title = "{A}lex-{U} 2023 {NLP} at {W}ojood{NER} shared task: {A}ra{BINDER} (Bi-encoder for {A}rabic Named Entity Recognition)",
author = "Hussein, Mariam and
Khaled, Sarah and
Torki, Marwan and
El-Makky, Nagwa",
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-1.90",
doi = "10.18653/v1/2023.arabicnlp-1.90",
pages = "797--802",
abstract = "Named Entity Recognition (NER) is a crucial task in natural language processing that facilitates the extraction of vital information from text. However, NER for Arabic presents a significant challenge due to the language{'}s unique characteristics. In this paper, we introduce AraBINDER, our submission to the Wojood NER Shared Task 2023 (ArabicNLP 2023). The shared task comprises two sub-tasks: sub-task 1 focuses on Flat NER, while sub-task 2 centers on Nested NER. We have participated in both sub-tasks. The Bi-Encoder has proven its efficiency for NER in English. We employ AraBINDER (Arabic Bi-Encoder for Named Entity Recognition), which uses the power of two transformer encoders and employs contrastive learning to map candidate text spans and entity types into the same vector representation space. This approach frames NER as a representation learning problem that maximizes the similarity between the vector representations of an entity mention and its type. Our experiments reveal that AraBINDER achieves a micro F-1 score of 0.918 for Flat NER and 0.9 for Nested NER on the Wojood dataset.",
}
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<abstract>Named Entity Recognition (NER) is a crucial task in natural language processing that facilitates the extraction of vital information from text. However, NER for Arabic presents a significant challenge due to the language’s unique characteristics. In this paper, we introduce AraBINDER, our submission to the Wojood NER Shared Task 2023 (ArabicNLP 2023). The shared task comprises two sub-tasks: sub-task 1 focuses on Flat NER, while sub-task 2 centers on Nested NER. We have participated in both sub-tasks. The Bi-Encoder has proven its efficiency for NER in English. We employ AraBINDER (Arabic Bi-Encoder for Named Entity Recognition), which uses the power of two transformer encoders and employs contrastive learning to map candidate text spans and entity types into the same vector representation space. This approach frames NER as a representation learning problem that maximizes the similarity between the vector representations of an entity mention and its type. Our experiments reveal that AraBINDER achieves a micro F-1 score of 0.918 for Flat NER and 0.9 for Nested NER on the Wojood dataset.</abstract>
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%0 Conference Proceedings
%T Alex-U 2023 NLP at WojoodNER shared task: AraBINDER (Bi-encoder for Arabic Named Entity Recognition)
%A Hussein, Mariam
%A Khaled, Sarah
%A Torki, Marwan
%A El-Makky, Nagwa
%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 hussein-etal-2023-alex
%X Named Entity Recognition (NER) is a crucial task in natural language processing that facilitates the extraction of vital information from text. However, NER for Arabic presents a significant challenge due to the language’s unique characteristics. In this paper, we introduce AraBINDER, our submission to the Wojood NER Shared Task 2023 (ArabicNLP 2023). The shared task comprises two sub-tasks: sub-task 1 focuses on Flat NER, while sub-task 2 centers on Nested NER. We have participated in both sub-tasks. The Bi-Encoder has proven its efficiency for NER in English. We employ AraBINDER (Arabic Bi-Encoder for Named Entity Recognition), which uses the power of two transformer encoders and employs contrastive learning to map candidate text spans and entity types into the same vector representation space. This approach frames NER as a representation learning problem that maximizes the similarity between the vector representations of an entity mention and its type. Our experiments reveal that AraBINDER achieves a micro F-1 score of 0.918 for Flat NER and 0.9 for Nested NER on the Wojood dataset.
%R 10.18653/v1/2023.arabicnlp-1.90
%U https://aclanthology.org/2023.arabicnlp-1.90
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.90
%P 797-802
Markdown (Informal)
[Alex-U 2023 NLP at WojoodNER shared task: AraBINDER (Bi-encoder for Arabic Named Entity Recognition)](https://aclanthology.org/2023.arabicnlp-1.90) (Hussein et al., ArabicNLP-WS 2023)
ACL