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

Researchers proposed CLUE, a contrastive learning framework for unsupervised event type induction, naming, and annotation, detailed in arXiv paper 2610.12580v1. CLUE combines a joint embedding representation capturing both verb-centric and semantic views of events with contrastive-loss-guided clustering, followed by event naming and refinement, to remove the need for domain experts to manually build event ontologies. The code is available at https://github.com/PratikMaitra/CLUE.

arXiv:2610.12580v1 Announce Type: new 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 https://github.com/PratikMaitra/CLUE.