BKEE: Pioneering Event Extraction in the Vietnamese Language

Thi-Nhung Nguyen, Bang Tien Tran, Trong-Nghia Luu, Thien Huu Nguyen, Kiem-Hieu Nguyen


Abstract
Event Extraction (EE) is a fundamental task in information extraction, aimed at identifying events and their associated arguments within textual data. It holds significant importance in various applications and serves as a catalyst for the development of related tasks. Despite the availability of numerous datasets and methods for event extraction in various languages, there has been a notable absence of a dedicated dataset for the Vietnamese language. To address this limitation, we propose BKEE, a novel event extraction dataset for Vietnamese. BKEE encompasses over 33 distinct event types and 28 different event argument roles, providing a labeled dataset for entity mentions, event mentions, and event arguments on 1066 documents. Additionally, we establish robust baselines for potential downstream tasks on this dataset, facilitating the analysis of challenges and future development prospects in the field of Vietnamese event extraction.
Anthology ID:
2024.lrec-main.217
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
2421–2427
Language:
URL:
https://aclanthology.org/2024.lrec-main.217
DOI:
Bibkey:
Cite (ACL):
Thi-Nhung Nguyen, Bang Tien Tran, Trong-Nghia Luu, Thien Huu Nguyen, and Kiem-Hieu Nguyen. 2024. BKEE: Pioneering Event Extraction in the Vietnamese Language. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2421–2427, Torino, Italia. ELRA and ICCL.
Cite (Informal):
BKEE: Pioneering Event Extraction in the Vietnamese Language (Nguyen et al., LREC-COLING 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.lrec-main.217.pdf