muNERa at WojoodNER 2024: Multi-tasking NER Approach
Nouf Alotaibi, Haneen Alhomoud, Hanan Murayshid, Waad Alshammari, Nouf Alshalawi, Sakhar Alkhereyf
Abstract
This paper presents our system “muNERa”, submitted to the WojoodNER 2024 shared task at the second ArabicNLP conference. We participated in two subtasks, the flat and nested fine-grained NER sub-tasks (1 and 2). muNERa achieved first place in the nested NER sub-task and second place in the flat NER sub-task. The system is based on the TANL framework (CITATION),by using a sequence-to-sequence structured language translation approach to model both tasks. We utilize the pre-trained AraT5v2-base model as the base model for the TANL framework. The best-performing muNERa model achieves 91.07% and 90.26% for the F-1 scores on the test sets for the nested and flat subtasks, respectively.- Anthology ID:
- 2024.arabicnlp-1.102
- Volume:
- Proceedings of The Second Arabic Natural Language Processing Conference
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Nizar Habash, Houda Bouamor, Ramy Eskander, Nadi Tomeh, Ibrahim Abu Farha, Ahmed Abdelali, Samia Touileb, Injy Hamed, Yaser Onaizan, Bashar Alhafni, Wissam Antoun, Salam Khalifa, Hatem Haddad, Imed Zitouni, Badr AlKhamissi, Rawan Almatham, Khalil Mrini
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 858–866
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.102
- DOI:
- 10.18653/v1/2024.arabicnlp-1.102
- Bibkey:
- Cite (ACL):
- Nouf Alotaibi, Haneen Alhomoud, Hanan Murayshid, Waad Alshammari, Nouf Alshalawi, and Sakhar Alkhereyf. 2024. muNERa at WojoodNER 2024: Multi-tasking NER Approach. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 858–866, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- muNERa at WojoodNER 2024: Multi-tasking NER Approach (Alotaibi et al., ArabicNLP-WS 2024)
- Copy Citation:
- PDF:
- https://aclanthology.org/2024.arabicnlp-1.102.pdf
Export citation
@inproceedings{alotaibi-etal-2024-munera,
title = "mu{NER}a at {W}ojood{NER} 2024: Multi-tasking {NER} Approach",
author = "Alotaibi, Nouf and
Alhomoud, Haneen and
Murayshid, Hanan and
Alshammari, Waad and
Alshalawi, Nouf and
Alkhereyf, Sakhar",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.102",
doi = "10.18653/v1/2024.arabicnlp-1.102",
pages = "858--866",
abstract = "This paper presents our system {``}muNERa{''}, submitted to the WojoodNER 2024 shared task at the second ArabicNLP conference. We participated in two subtasks, the flat and nested fine-grained NER sub-tasks (1 and 2). muNERa achieved first place in the nested NER sub-task and second place in the flat NER sub-task. The system is based on the TANL framework (CITATION),by using a sequence-to-sequence structured language translation approach to model both tasks. We utilize the pre-trained AraT5v2-base model as the base model for the TANL framework. The best-performing muNERa model achieves 91.07{\%} and 90.26{\%} for the F-1 scores on the test sets for the nested and flat subtasks, respectively.",
}
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%0 Conference Proceedings %T muNERa at WojoodNER 2024: Multi-tasking NER Approach %A Alotaibi, Nouf %A Alhomoud, Haneen %A Murayshid, Hanan %A Alshammari, Waad %A Alshalawi, Nouf %A Alkhereyf, Sakhar %Y Habash, Nizar %Y Bouamor, Houda %Y Eskander, Ramy %Y Tomeh, Nadi %Y Abu Farha, Ibrahim %Y Abdelali, Ahmed %Y Touileb, Samia %Y Hamed, Injy %Y Onaizan, Yaser %Y Alhafni, Bashar %Y Antoun, Wissam %Y Khalifa, Salam %Y Haddad, Hatem %Y Zitouni, Imed %Y AlKhamissi, Badr %Y Almatham, Rawan %Y Mrini, Khalil %S Proceedings of The Second Arabic Natural Language Processing Conference %D 2024 %8 August %I Association for Computational Linguistics %C Bangkok, Thailand %F alotaibi-etal-2024-munera %X This paper presents our system “muNERa”, submitted to the WojoodNER 2024 shared task at the second ArabicNLP conference. We participated in two subtasks, the flat and nested fine-grained NER sub-tasks (1 and 2). muNERa achieved first place in the nested NER sub-task and second place in the flat NER sub-task. The system is based on the TANL framework (CITATION),by using a sequence-to-sequence structured language translation approach to model both tasks. We utilize the pre-trained AraT5v2-base model as the base model for the TANL framework. The best-performing muNERa model achieves 91.07% and 90.26% for the F-1 scores on the test sets for the nested and flat subtasks, respectively. %R 10.18653/v1/2024.arabicnlp-1.102 %U https://aclanthology.org/2024.arabicnlp-1.102 %U https://doi.org/10.18653/v1/2024.arabicnlp-1.102 %P 858-866
Markdown (Informal)
[muNERa at WojoodNER 2024: Multi-tasking NER Approach](https://aclanthology.org/2024.arabicnlp-1.102) (Alotaibi et al., ArabicNLP-WS 2024)
- muNERa at WojoodNER 2024: Multi-tasking NER Approach (Alotaibi et al., ArabicNLP-WS 2024)
ACL
- Nouf Alotaibi, Haneen Alhomoud, Hanan Murayshid, Waad Alshammari, Nouf Alshalawi, and Sakhar Alkhereyf. 2024. muNERa at WojoodNER 2024: Multi-tasking NER Approach. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 858–866, Bangkok, Thailand. Association for Computational Linguistics.