关键词: 因果关系抽取, 时间关系, 序列标注, 图卷积, 双向长短期记忆网络(Bi-LSTM)

Abstract: Aiming at the problem that traditional time relationships were only applied in the direction of machine learning, we proposed a relationship extraction method based on sequence labeling entity recognition. We first constructed Bi-LSTM model for feature extraction, and then input time relationship as a characteristic matrix for graph convolution. The experimental results show that the time relationship can improve the effect of causality extraction, and the Bi-LSTM+GCN model containing time relationship can effectively extract causal events, and the results of causality extraction of the Bi-LSTM+GCN model with time relationship are better than those of traditional methods.

Key words: causality extraction, time relationship, sequence labeling, graph convolution, bidirectional long short-term memory (Bi-LSTM)