Abstract
Previous work on Arabic information extraction has mainly focused on named entity recognition and very little work has been done on Arabic relation extraction and event recognition. Moreover, modeling Arabic data for such tasks is not straightforward because of the morphological richness and idiosyncrasies of the Arabic language. We propose in this article the first neural joint information extraction system for the Arabic language.- Anthology ID:
- 2022.wanlp-1.31
- Volume:
- Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 331–345
- Language:
- URL:
- https://aclanthology.org/2022.wanlp-1.31
- DOI:
- 10.18653/v1/2022.wanlp-1.31
- Cite (ACL):
- Niama El Khbir, Nadi Tomeh, and Thierry Charnois. 2022. ArabIE: Joint Entity, Relation and Event Extraction for Arabic. In Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP), pages 331–345, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- ArabIE: Joint Entity, Relation and Event Extraction for Arabic (El Khbir et al., WANLP 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.wanlp-1.31.pdf