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

Computer Science > Computation and Language arXiv:2610.12580 (cs) [Submitted on 8 Oct 2026] Title: CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation Authors: Pratik Maitra, Lauren Kwon, Qi Li.

计算机科学 > 计算与语言 arXiv:2610.12580 (cs) [提交于 2026 年 10 月 8 日] 标题：CLUE：一种用于无监督事件类型归纳与标注的对比学习框架 作者：Pratik Maitra, Lauren Kwon, Qi Li。

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.

摘要：事件本体是定义数据集中事件的基础框架。事件类型归纳通常涉及领域专家，他们根据数据集的领域和范围制定创建事件类型及其名称的指南。然而，这一过程在标注成本方面资源密集，且容易产生人为偏差。

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.

为了减轻对人工创建事件本体的需求，我们提出了用于事件类型归纳、命名和标注的 CLUE 框架。我们在本工作中的主要贡献包括：1) 设计了一种新颖的联合嵌入表示，用于表达事件的动词中心视图和语义视图；2) 使用对比损失引导聚类进行事件类型归纳；3) 事件命名与优化。

Experiments demonstrate the effectiveness of our method. Our code is available at this https URL. Subjects: Computation and Language (cs.CL).

实验证明了我们方法的有效性。我们的代码可在该 https 链接获取。学科：计算与语言 (cs.CL)。