Event Semantic Classification in Context
Haoyu Wang, Hongming Zhang, Kaiqiang Song, Dong Yu, Dan Roth
Abstract
In this work, we focus on a fundamental yet underexplored problem, event semantic classification in context, to help machines gain a deeper understanding of events. We classify events from six perspectives: modality, affirmation, specificity, telicity, durativity, and kinesis. These properties provide essential cues regarding the occurrence and grounding of events, changes of status that events can bring about, and the connection between events and time. To this end, this paper introduces a novel dataset collected for the semantic classification tasks and several effective models. By incorporating these event properties into downstream tasks, we demonstrate that understanding the fine-grained event semantics benefits downstream event understanding and reasoning via experiments on event extraction, temporal relation extraction, and subevent relation extraction.- Anthology ID:
- 2024.findings-eacl.94
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2024
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1395–1407
- Language:
- URL:
- https://aclanthology.org/2024.findings-eacl.94
- DOI:
- Cite (ACL):
- Haoyu Wang, Hongming Zhang, Kaiqiang Song, Dong Yu, and Dan Roth. 2024. Event Semantic Classification in Context. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1395–1407, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Event Semantic Classification in Context (Wang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-eacl.94.pdf