Event Ontology Completion with Hierarchical Structure Evolution Networks

Pengfei Cao, Yupu Hao, Yubo Chen, Kang Liu, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Jun Zhao

Submission Type: Regular Long Paper

Submission Track: NLP Applications

Submission Track 2: Information Extraction

Keywords: Event Ontology Completion, Event Type Induction, Hierarchy Expansion, Type Naming

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.

Submission Number: 1035