专家注释数据集的验证方法:事件注释案例研究

O. Inel, Lora Aroyo
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引用次数: 11

摘要

由于事件实体的复杂性和模糊性,事件检测仍然是一项困难的任务。一方面,我们观察到,在注释事件时,专家之间的注释者之间的一致性很低,忽略了现有的大量注释指南及其无数的修订。另一方面,与其他类型的实体(如人或地点)相比,事件提取系统在f1得分方面的测量性能较低。本文研究了事件和时间表达式的专家注释数据集的一致性和完备性。我们在一致性和完整性方面提出了这种数据集的数据不可知验证方法。此外,我们结合了人群和机器的力量来纠正和扩展专家注释的事件数据集。我们展示了使用群体注释事件来训练和评估最先进的事件提取系统的好处。我们的结果表明,群体注释事件使系统的性能至少提高了5.3%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validation Methodology for Expert-Annotated Datasets: Event Annotation Case Study
Event detection is still a difficult task due to the complexity and the ambiguity of such entities. On the one hand, we observe a low inter-annotator agreement among experts when annotating events, disregarding the multitude of existing annotation guidelines and their numerous revisions. On the other hand, event extraction systems have a lower measured performance in terms of F1-score compared to other types of entities such as people or locations. In this paper we study the consistency and completeness of expert-annotated datasets for events and time expressions. We propose a data-agnostic validation methodology of such datasets in terms of consistency and completeness. Furthermore, we combine the power of crowds and machines to correct and extend expert-annotated datasets of events. We show the benefit of using crowd-annotated events to train and evaluate a state-of-the-art event extraction system. Our results show that the crowd-annotated events increase the performance of the system by at least 5.3%.
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