基于图卷积神经网络的中文事件时序关系分类

In the task of event temporal relationship, the Chinese dataset is relatively scarce compared with the English dataset, resulting in the relatively lagging research of event temporal relationship classification of Chinese text. In this paper, the classification of event temporal relations is explored on the Chinese emergency corpus CEC. The traditional deep learning method is good at processing data in European space, and there may be multi-dimensional characteristics such as species and dependency between events, which can better reflect the meaning of sentences in non-European space. For this reason, this paper proposes a Chinese event temporal relationship classification method based on graph convolution neural network, which constructs one-dimensional text statements into multidimensional graph structure data, and trains in the convolution neural network layer to complete the classification work. Experiments on CEC data sets show that the performance of this method is better than other baseline tasks, and better experimental results are achieved.