基于事件动作方向的隐式因果关系抽取方法

抽取事件之间的因果关系能够应用于自动问答、知识提取、常识推理等方面.隐式因果关系由于缺乏明显的词汇特征和中文复杂的句法结构,使得其抽取极为困难,已成为当前研究的难点.相比而言,显示因果关系的抽取比较容易、准确率高,且因果关系事件之间的逻辑关系稳定.为此,文中提出了一种原创的方法,首先通过对抽取的显示因果事件对进行事件动...

Implicit Causality Extraction Method Based on Event Action Direction

Extracting the causality between events can be applied to automatic question answering,knowledge extraction,common sense reasoning and so on.Due to the lack of obvious lexical features and the complex syntactic structure of Chinese,it is very difficult to extract implicit causality,which has become the bottleneck of the current research.In contrast,it is easy to extract explicit causality with high accuracy,and the logical causal relationship between events is stable.Therefore,an original method is proposed in this paper.Firstly,the extracted explicit causal event pairs are normalized to form the event direction,and then the event subject is generalized to form a standard set of matched causal event pairs.This set is used to extract implicit causal event pairs according to event similarity.In order to identify more implicit causality,a new causal connectives discovery algorithm is proposed.The experimental data crawling on NetEase Finance,Tencent Finance and Sina Finance show that the extraction precision is improved by 1.02%compared with the traditional method.