Schema-based event extraction is a critical technique to apprehend the essential content of events promptly. With the rapid development of deep learning technology, event extraction technology based on deep learning has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models. We summarize the task definition, paradigm, and models of schema-based event extraction and then discuss each of these in detail. We introduce benchmark datasets that support tests of predictions and evaluation metrics. A comprehensive comparison between different techniques is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.
翻译:基于 Schema 事件提取是迅速掌握事件基本内容的关键技术。随着深层学习技术的迅速发展,基于深层学习的事件提取技术已成为研究热点。文献中提出了许多方法、数据集和评价指标,提高了全面和更新调查的必要性。本文件通过审查最新方法填补了差距,侧重于深层学习模型。我们总结了基于计划事件提取的任务定义、模式和模式,然后详细讨论了其中的每一项。我们引入了支持预测和评价指标测试的基准数据集。本次调查还全面比较了不同技术。最后,我们总结了研究领域今后的研究方向。