论文标题
有效事件检测的语义枢纽模型
Semantic Pivoting Model for Effective Event Detection
论文作者
论文摘要
事件检测旨在识别和分类从非结构化文章中提及事件实例，是自然语言处理（NLP）的重要任务。现有的事件检测技术仅使用均匀的单热量向量来表示事件类型类，而忽略了类型的语义含义对任务很重要的事实。这种方法效率低下，容易过度拟合。在本文中，我们提出了一个用于有效事件检测（速度）的语义透视模型，该模型在训练过程中明确合并了先前的信息，并捕获了输入和事件之间的语义有意义的相关性。实验结果表明，我们提出的模型实现了最新的性能，并在不使用任何外部资源的情况下超过了多个设置的基准。
Event Detection, which aims to identify and classify mentions of event instances from unstructured articles, is an important task in Natural Language Processing (NLP). Existing techniques for event detection only use homogeneous one-hot vectors to represent the event type classes, ignoring the fact that the semantic meaning of the types is important to the task. Such an approach is inefficient and prone to overfitting. In this paper, we propose a Semantic Pivoting Model for Effective Event Detection (SPEED), which explicitly incorporates prior information during training and captures semantically meaningful correlations between input and events. Experimental results show that our proposed model achieves state-of-the-art performance and outperforms the baselines in multiple settings without using any external resources.
