Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset

• DOI: 10.18653/v1/2022.emnlp-main.437
• Publication Date: 2023-08-04T20:21:02Z
• Authors: Haolin Deng; Yanan Zhang; Yangfan Zhang; Wangyang Ying; Changlong Yu; Jun Gao; Wei Wang; Xiaoling Bai; Nan Yang; Jin Ma; Xiang Chen; Tianhua Zhou
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Computation and Language (cs.CL)

Event extraction (EE) is crucial to downstream tasks such as new aggregation and event knowledge graph construction. Most existing EE datasets manually define fixed event types and design specific schema for each of them, failing to cover diverse events emerging from the online text. Moreover, news titles, an important source of event mentions, have not gained enough attention in current EE research. In this paper, We present Title2Event, a large-scale sentence-level dataset benchmarking Open Event Extraction without restricting event types. Title2Event contains more than 42,000 news titles in 34 topics collected from Chinese web pages. To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction. We further conduct experiments on Title2Event with different models and show that the characteristics of titles make it challenging for event extraction, addressing the significance of advanced study on this problem. The dataset and baseline codes are available at https://open-event-hub.github.io/title2event.<br/>EMNLP 2022<br/>