DRU at WojoodNER 2024: A Multi-level Method Approach
Hadi Hamoud, Chadi Chakra, Nancy Hamdan, Osama Mraikhat, Doha Albared, Fadi Zaraket
Abstract
In this paper, we present our submission for the WojoodNER 2024 Shared Tasks addressing flat and nested sub-tasks (1, 2). We experiment with three different approaches. We train (i) an Arabic fine-tuned version of BLOOMZ-7b-mt, GEMMA-7b, and AraBERTv2 on multi-label token classifications task; (ii) two AraBERTv2 models, on main types and sub-types respectively; and (iii) one model for main types and four for the four sub-types. Based on the Wojood NER 2024 test set results, the three fine-tuned models performed similarly with AraBERTv2 favored (F1: Flat=.8780 Nested=.9040). The five model approach performed slightly better (F1: Flat=.8782 Nested=.9043).- Anthology ID:
- 2024.arabicnlp-1.104
- 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:
- 874–879
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.104
- DOI:
- Bibkey:
- Cite (ACL):
- Hadi Hamoud, Chadi Chakra, Nancy Hamdan, Osama Mraikhat, Doha Albared, and Fadi Zaraket. 2024. DRU at WojoodNER 2024: A Multi-level Method Approach. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 874–879, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- DRU at WojoodNER 2024: A Multi-level Method Approach (Hamoud et al., ArabicNLP-WS 2024)
- Copy Citation:
- PDF:
- https://aclanthology.org/2024.arabicnlp-1.104.pdf
Export citation
@inproceedings{hamoud-etal-2024-dru,
title = "{DRU} at {W}ojood{NER} 2024: A Multi-level Method Approach",
author = "Hamoud, Hadi and
Chakra, Chadi and
Hamdan, Nancy and
Mraikhat, Osama and
Albared, Doha and
Zaraket, Fadi",
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-1.104",
pages = "874--879",
abstract = "In this paper, we present our submission for the WojoodNER 2024 Shared Tasks addressing flat and nested sub-tasks (1, 2). We experiment with three different approaches. We train (i) an Arabic fine-tuned version of BLOOMZ-7b-mt, GEMMA-7b, and AraBERTv2 on multi-label token classifications task; (ii) two AraBERTv2 models, on main types and sub-types respectively; and (iii) one model for main types and four for the four sub-types. Based on the Wojood NER 2024 test set results, the three fine-tuned models performed similarly with AraBERTv2 favored (F1: Flat=.8780 Nested=.9040). The five model approach performed slightly better (F1: Flat=.8782 Nested=.9043).",
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%0 Conference Proceedings %T DRU at WojoodNER 2024: A Multi-level Method Approach %A Hamoud, Hadi %A Chakra, Chadi %A Hamdan, Nancy %A Mraikhat, Osama %A Albared, Doha %A Zaraket, Fadi %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 hamoud-etal-2024-dru %X In this paper, we present our submission for the WojoodNER 2024 Shared Tasks addressing flat and nested sub-tasks (1, 2). We experiment with three different approaches. We train (i) an Arabic fine-tuned version of BLOOMZ-7b-mt, GEMMA-7b, and AraBERTv2 on multi-label token classifications task; (ii) two AraBERTv2 models, on main types and sub-types respectively; and (iii) one model for main types and four for the four sub-types. Based on the Wojood NER 2024 test set results, the three fine-tuned models performed similarly with AraBERTv2 favored (F1: Flat=.8780 Nested=.9040). The five model approach performed slightly better (F1: Flat=.8782 Nested=.9043). %U https://aclanthology.org/2024.arabicnlp-1.104 %P 874-879
Markdown (Informal)
[DRU at WojoodNER 2024: A Multi-level Method Approach](https://aclanthology.org/2024.arabicnlp-1.104) (Hamoud et al., ArabicNLP-WS 2024)
- DRU at WojoodNER 2024: A Multi-level Method Approach (Hamoud et al., ArabicNLP-WS 2024)
ACL
- Hadi Hamoud, Chadi Chakra, Nancy Hamdan, Osama Mraikhat, Doha Albared, and Fadi Zaraket. 2024. DRU at WojoodNER 2024: A Multi-level Method Approach. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 874–879, Bangkok, Thailand. Association for Computational Linguistics.