CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation

Abstract:Event ontology is the foundational framework for defining events in a dataset. Event type induction generally involves domain experts who define guidelines for creating event types and their names based on the dataset's domain and scope. However, this process is resource-intensive in terms of annotation costs and is prone to human biases. To alleviate the need for manual event ontology creation, we propose the CLUE framework for event type induction, naming, and annotation. Our main contributions in this work are: 1) designing a novel joint embedding representation for expressing both the verb-centric view and the semantic view of events; 2) event type induction using contrastive loss guided clustering; 3) event naming and refinement. Experiments demonstrate the effectiveness of our method. Our code is available at this https URL.

Computation and Language (cs.CL)

arXiv:2610.12580 [cs.CL]

(or arXiv:2610.12580v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.12580 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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