论文标题
事件参数通过扩张的封闭卷积神经网络提取具有增强本地特征的卷积卷积神经网络
Event Arguments Extraction via Dilate Gated Convolutional Neural Network with Enhanced Local Features
论文作者
论文摘要
事件提取在信息算法中起着重要的作用，以了解世界。事件提取可以分为两个子任务：一个是事件触发提取，另一个是事件参数提取。但是，事件参数提取的F-评分远低于事件触发提取的f得分，即在最新工作中，事件触发触发提取可实现80.7％，而事件参数提取仅可实现58％。在管道结构中，事件参数提取的困难在于其缺乏分类功能以及更高的计算消耗。在这项工作中，我们提出了一种基于多层扩张式卷积神经网络（EE-DGCNN）的新型事件提取方法，该方法的参数较少。此外，增强的本地信息被整合到Word功能中，以为第一个子任务预测的触发器分配事件参数角色。数值实验表明，除了最先进的事件提取方法外，在现实世界数据集上进行了显着改进的性能。提出了提取程序的进一步分析，并进行了实验，以分析与性能改善有关的影响因素。
Event Extraction plays an important role in information-extraction to understand the world. Event extraction could be split into two subtasks: one is event trigger extraction, the other is event arguments extraction. However, the F-Score of event arguments extraction is much lower than that of event trigger extraction, i.e. in the most recent work, event trigger extraction achieves 80.7%, while event arguments extraction achieves only 58%. In pipelined structures, the difficulty of event arguments extraction lies in its lack of classification feature, and the much higher computation consumption. In this work, we proposed a novel Event Extraction approach based on multi-layer Dilate Gated Convolutional Neural Network (EE-DGCNN) which has fewer parameters. In addition, enhanced local information is incorporated into word features, to assign event arguments roles for triggers predicted by the first subtask. The numerical experiments demonstrated significant performance improvement beyond state-of-art event extraction approaches on real-world datasets. Further analysis of extraction procedure is presented, as well as experiments are conducted to analyze impact factors related to the performance improvement.
