CLUE：用于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总结：
针对手动事件本体创建成本高、易有偏差的问题，提出CLUE框架，通过联合嵌入表示、对比损失引导聚类及事件命名优化，实现无监督事件类型归纳与标注，方法经实验验证有效。
摘要：
事件本体是定义数据集中事件的基础框架。事件类型归纳通常需要领域专家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，但该过程在标注成本方面资源密集，且易受人为偏差影响。为减少手动创建事件本体的需求，本文提出用于事件类型归纳、命名与标注的CLUE框架。本文主要贡献包括：1）设计新颖的联合嵌入表示，以表达事件的动词中心视图和语义视图；2）采用对比损失引导的聚类进行事件类型归纳；3）事件命名与优化。实验验证了该方法的有效性，代码可在指定URL获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
