@inproceedings{deng-etal-2021-ontoed,
title = "{O}nto{ED}: Low-resource Event Detection with Ontology Embedding",
author = "Deng, Shumin and
Zhang, Ningyu and
Li, Luoqiu and
Hui, Chen and
Huaixiao, Tou and
Chen, Mosha and
Huang, Fei and
Chen, Huajun",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.220",
doi = "10.18653/v1/2021.acl-long.220",
pages = "2828--2839",
abstract = "Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.",
}
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<abstract>Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.</abstract>
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%0 Conference Proceedings
%T OntoED: Low-resource Event Detection with Ontology Embedding
%A Deng, Shumin
%A Zhang, Ningyu
%A Li, Luoqiu
%A Hui, Chen
%A Huaixiao, Tou
%A Chen, Mosha
%A Huang, Fei
%A Chen, Huajun
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F deng-etal-2021-ontoed
%X Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.
%R 10.18653/v1/2021.acl-long.220
%U https://aclanthology.org/2021.acl-long.220
%U https://doi.org/10.18653/v1/2021.acl-long.220
%P 2828-2839
Markdown (Informal)
[OntoED: Low-resource Event Detection with Ontology Embedding](https://aclanthology.org/2021.acl-long.220) (Deng et al., ACL-IJCNLP 2021)
ACL
- Shumin Deng, Ningyu Zhang, Luoqiu Li, Chen Hui, Tou Huaixiao, Mosha Chen, Fei Huang, and Huajun Chen. 2021. OntoED: Low-resource Event Detection with Ontology Embedding. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2828–2839, Online. Association for Computational Linguistics.