Abstract
Relational entity extraction is key in building knowledge graphs. A relational entity has a source, a tail and atype. In this paper, we consider Arabic text and introduce evidence enrichment which intuitivelyinforms models for better predictions. Relational evidence is an expression in the textthat explains how sources and targets relate. %It also provides hints from which models learn. This paper augments the existing relational extraction dataset with evidence annotation to its 2.9-million Arabic relations.We leverage the augmented dataset to build , a relation extraction with evidence model from Arabic documents. The evidence augmentation model we constructed to complete the dataset achieved .82 F1-score (.93 precision, .73 recall). The target outperformed SOTA mREBEL with .72 F1-score (.78 precision, .66 recall).- Anthology ID:
- 2024.arabicnlp-1.6
- 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:
- 67–72
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.6
- DOI:
- 10.18653/v1/2024.arabicnlp-1.6
- Cite (ACL):
- Osama Mraikhat, Hadi Hamoud, and Fadi Zaraket. 2024. AREEj: Arabic Relation Extraction with Evidence. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 67–72, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- AREEj: Arabic Relation Extraction with Evidence (Mraikhat et al., ArabicNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.arabicnlp-1.6.pdf