Temporal and causal relations play an important role in determining the dependencies between events. Classifying the temporal and causal relations between events has many applications, such as generating event timelines, event summarization, textual entailment and question answering. Temporal and causal relations are closely related and influence each other. So we propose a joint model that incorporates both temporal and causal features to perform causal relation classification. We use the syntactic structure of the text for identifying temporal and causal relations between two events from the text. We extract parts-of-speech tag sequence, dependency tag sequence and word sequence from the text. We propose an LSTM based model for temporal and causal relation classification that captures the interrelations between the three encoded features. Evaluation of our model on four popular datasets yields promising results for temporal and causal relation classification.
翻译:时间和因果关系在确定事件之间的依存关系方面起着重要作用。 区分事件之间的时间和因果关系有许多应用, 例如产生事件时间、事件总和、文字要求和答题等。 时间和因果关系密切相关, 并相互影响。 因此, 我们提出一个包含时间和因果特点的联合模型来进行因果关系分类。 我们使用文本的合成结构来确定两个事件之间的时间和因果关系。 我们从文本中提取部分语音标记序列、 依赖性标记序列和字数顺序。 我们提出了一个基于时间和因果关系分类的LSTM模型, 以捕捉三个编码特征之间的相互关系。 对四种流行数据集模型的评估为时间和因果关系分类带来了良好的结果。