Word-level Commonsense Knowledge Selection for Event Detection

Shuai Yang, Yu Hong, Shiming He, Qingting Xu, Jianmin Yao

Abstract
Event Detection (ED) is a task of automatically extracting multi-class trigger words. The understanding of word sense is crucial for ED. In this paper, we utilize context-specific commonsense knowledge to strengthen word sense modeling. Specifically, we leverage a Context-specific Knowledge Selector (CKS) to select the exact commonsense knowledge of words from a large knowledge base, i.e., ConceptNet. Context-specific selection is made in terms of the relevance of knowledge to the living contexts. On this basis, we incorporate the commonsense knowledge into the word-level representations before decoding. ChatGPT is an ideal generative CKS when the prompts are deliberately designed, though it is cost-prohibitive. To avoid the heavy reliance on ChatGPT, we train an offline CKS using the predictions of ChatGPT over a small number of examples (about 9% of all). We experiment on the benchmark ACE-2005 dataset. The test results show that our approach yields substantial improvements compared to the BERT baseline, achieving the F1-score of about 78.3%. All models, source codes and data will be made publicly available.
Anthology ID:
2024.lrec-main.1537
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:
17675–17682
Language:
URL:
https://aclanthology.org/2024.lrec-main.1537
DOI:
Bibkey:
Cite (ACL):
Shuai Yang, Yu Hong, Shiming He, Qingting Xu, and Jianmin Yao. 2024. Word-level Commonsense Knowledge Selection for Event Detection. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17675–17682, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Word-level Commonsense Knowledge Selection for Event Detection (Yang et al., LREC-COLING 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1537.pdf
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