Document-Level Event Extraction via Information Interaction Based on Event Relation and Argument Correlation
Bangze Pan, Yang Li, Suge Wang, Xiaoli Li, Deyu Li, Jian Liao, Jianxing Zheng
Abstract
Document-level Event Extraction (DEE) is a vital task in NLP as it seeks to automatically recognize and extract event information from a document. However, current approaches often overlook intricate relationships among events and subtle correlations among arguments within a document, which can significantly impact the effectiveness of event type recognition and the extraction of cross-sentence arguments in DEE task. This paper proposes a novel Correlation Association Interactive Network (CAINet), comprising two key components: event relationship graph and argument correlation graph. In particular, the event relationship graph models the relationship among various events through structural associations among event nodes and sentence nodes, to improve the accuracy of event recognition. On the other hand, the arguments correlation graph models the correlations among arguments by quantifying the strength of association among arguments, to effectively aggregate cross-sentence arguments, contributing to the overall success of DEE. Furthermore, we use the large language model to execute DEE task experiments. Experimental results show the proposed CAINet outperforms existing state-of-the-art models and large language models in terms of F1-score across two benchmark datasets.- Anthology ID:
- 2024.lrec-main.459
- 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:
- 5156–5166
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.459
- DOI:
- Cite (ACL):
- Bangze Pan, Yang Li, Suge Wang, Xiaoli Li, Deyu Li, Jian Liao, and Jianxing Zheng. 2024. Document-Level Event Extraction via Information Interaction Based on Event Relation and Argument Correlation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5156–5166, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Document-Level Event Extraction via Information Interaction Based on Event Relation and Argument Correlation (Pan et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2024.lrec-main.459.pdf