基于事件动作方向的隐式因果关系抽取方法  

Implicit Causality Extraction Method Based on Event Action Direction

作　　者：缪峰[1] 王萍[2] 李太勇[2] MIU Feng;WANG Ping;LI Tai-yong(School of Artificial Intelligence and Law,Southwest University of Political Science&Law,Chongqing 401120,China;School of Economic Information Engineering,Southwestern University of Finance and Economics,Chengdu 611130,China)

机构地区：[1]西南政法大学人工智能法学院,重庆401120 [2]西南财经大学经济信息工程学院,成都611130

出　　处：《计算机科学》2022年第3期276-280,共5页Computer Science

基　　金：教育部人文社会科学研究一般项目(19YJAZH047)。

摘　　要：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作的归一化处理后形成事件方向,然后对事件主体进行泛化处理,最终形成标准的匹配因果事件对集合。利用此集合根据事件相似度从语句中抽取隐式因果事件对。为了识别更多的隐式因果关系,文中同时提出了一种因果连接词发现算法。在网易财经、腾讯财经和新浪财经上爬取的实验数据验证,对事件动作进行归一化处理后形成事件方向相比传统方法抽取准确率提高了1.02%。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.

关 键 词：因果关系 因果连接词 事件抽取 事件动作 句法结构分析 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]