arXiv：cs.CL· Pratik Maitra, Lauren Kwon, Qi Li·· 11 小时前
CLUE：无监督事件类型归纳与标注的对比学习框架
CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation
AI 导读
CLUE 是一个用于事件类型归纳、命名与标注的框架，通过对比损失引导的聚类实现无监督事件类型归纳，以减少人工事件本体构建的需求。该框架设计了同时表达事件动词中心视角与语义视角的联合嵌入表示，并支持事件命名与优化，其实验验证了方法的有效性，代码已开源。
正文
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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12580 [cs.CL] |
| (or arXiv:2610.12580v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12580 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pratik Maitra [view email]
[v1]
Thu, 8 Oct 2026 18:05:16 UTC (382 KB)
来源：arXiv：cs.CL · arxiv.org