打造首个完整一致的事件关系标注工具，提升效率与准确性。
EventFull: Complete and Consistent Event Relation Annotation
- 统一流程同步标注时序、因果与指代关系
- 实测加速标注过程，标注者一致性高
- 适合需要高质量事件关系数据的研究者
事件关系识别是自然语言处理的基础任务，广泛应用于下游应用，其建模依赖于标注了多种类型事件关系的数据集。然而，由于需考虑事件对的二次数量，系统性且完整的标注成本高、难度大，导致许多现有数据集缺乏系统性和完整性。为此，我们提出 extit{EventFull}，首个支持通过统一协同流程实现时序、因果与指代关系一致、完整且高效的标注工具。试点研究显示，EventFull 显著加速并简化了标注过程，同时获得高标注者间一致性。
原文摘要 · Abstract (English)
Event relation detection is a fundamental NLP task, leveraged in many downstream applications, whose modeling requires datasets annotated with event relations of various types. However, systematic and complete annotation of these relations is costly and challenging, due to the quadratic number of event pairs that need to be considered. Consequently, many current event relation datasets lack systematicity and completeness. In response, we introduce \textit{EventFull}, the first tool that supports consistent, complete and efficient annotation of temporal, causal and coreference relations via a unified and synergetic process. A pilot study demonstrates that EventFull accelerates and simplifies the annotation process while yielding high inter-annotator agreement.
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