@inproceedings{wang-etal-2024-event,
title = "Event Semantic Classification in Context",
author = "Wang, Haoyu and
Zhang, Hongming and
Song, Kaiqiang and
Yu, Dong and
Roth, Dan",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-eacl.94",
pages = "1395--1407",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Event Semantic Classification in Context
%A Wang, Haoyu
%A Zhang, Hongming
%A Song, Kaiqiang
%A Yu, Dong
%A Roth, Dan
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F wang-etal-2024-event
%X 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.
%U https://aclanthology.org/2024.findings-eacl.94
%P 1395-1407
Markdown (Informal)
[Event Semantic Classification in Context](https://aclanthology.org/2024.findings-eacl.94) (Wang et al., Findings 2024)
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.