@inproceedings{yang-etal-2024-word-level,
title = "Word-level Commonsense Knowledge Selection for Event Detection",
author = "Yang, Shuai and
Hong, Yu and
He, Shiming and
Xu, Qingting and
Yao, Jianmin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1537",
pages = "17675--17682",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Word-level Commonsense Knowledge Selection for Event Detection
%A Yang, Shuai
%A Hong, Yu
%A He, Shiming
%A Xu, Qingting
%A Yao, Jianmin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F yang-etal-2024-word-level
%X 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.
%U https://aclanthology.org/2024.lrec-main.1537
%P 17675-17682
Markdown (Informal)
[Word-level Commonsense Knowledge Selection for Event Detection](https://aclanthology.org/2024.lrec-main.1537) (Yang et al., LREC-COLING 2024)
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.