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    <abstract>Traditional event detection methods require predefined event schemas. However, manually defining event schemas is expensive and the coverage of schemas is limited. To this end, some works study the event type induction (ETI) task, which discovers new event types via clustering. However, the setting of ETI suffers from two limitations: event types are not linked into the existing hierarchy and have no semantic names. In this paper, we propose a new research task named Event Ontology Completion (EOC), which aims to simultaneously achieve event clustering, hierarchy expansion and type naming. Furthermore, we develop a Hierarchical Structure Evolution Network (HalTon) for this new task. Specifically, we first devise a Neighborhood Contrastive Clustering module to cluster unlabeled event instances. Then, we propose a Hierarchy-Aware Linking module to incorporate the hierarchical information for event expansion. Finally, we generate meaningful names for new types via an In-Context Learning-based Naming module. Extensive experiments indicate that our method achieves the best performance, outperforming the baselines by 8.23%, 8.79% and 8.10% of ARI score on three datasets.</abstract>
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%0 Conference Proceedings
%T Event Ontology Completion with Hierarchical Structure Evolution Networks
%A Cao, Pengfei
%A Hao, Yupu
%A Chen, Yubo
%A Liu, Kang
%A Xu, Jiexin
%A Li, Huaijun
%A Jiang, Xiaojian
%A Zhao, Jun
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cao-etal-2023-event
%X Traditional event detection methods require predefined event schemas. However, manually defining event schemas is expensive and the coverage of schemas is limited. To this end, some works study the event type induction (ETI) task, which discovers new event types via clustering. However, the setting of ETI suffers from two limitations: event types are not linked into the existing hierarchy and have no semantic names. In this paper, we propose a new research task named Event Ontology Completion (EOC), which aims to simultaneously achieve event clustering, hierarchy expansion and type naming. Furthermore, we develop a Hierarchical Structure Evolution Network (HalTon) for this new task. Specifically, we first devise a Neighborhood Contrastive Clustering module to cluster unlabeled event instances. Then, we propose a Hierarchy-Aware Linking module to incorporate the hierarchical information for event expansion. Finally, we generate meaningful names for new types via an In-Context Learning-based Naming module. Extensive experiments indicate that our method achieves the best performance, outperforming the baselines by 8.23%, 8.79% and 8.10% of ARI score on three datasets.
%R 10.18653/v1/2023.emnlp-main.21
%U https://aclanthology.org/2023.emnlp-main.21
%U https://doi.org/10.18653/v1/2023.emnlp-main.21
%P 306-320

Markdown (Informal)

[Event Ontology Completion with Hierarchical Structure Evolution Networks](https://aclanthology.org/2023.emnlp-main.21) (Cao et al., EMNLP 2023)

• Event Ontology Completion with Hierarchical Structure Evolution Networks (Cao et al., EMNLP 2023)

ACL

• Pengfei Cao, Yupu Hao, Yubo Chen, Kang Liu, Jiexin Xu, Huaijun Li, Xiaojian Jiang, and Jun Zhao. 2023. Event Ontology Completion with Hierarchical Structure Evolution Networks. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 306–320, Singapore. Association for Computational Linguistics.