End-to-end multi-granulation causality extraction model  

作　　者：Miao Wu Qinghua Zhang Chengying Wu Guoyin Wang 

机构地区：[1]Chongqing Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism,Chongqing University of Posts and Telecommunications,Chongqing,400065,China [2]Chonging Key Laboratory of Computational Intlligence,Chongqing University of Posts and Telecommunications,Chongqing,400065,China

出　　处：《Digital Communications and Networks》2024年第6期1864-1873,共10页数字通信与网络(英文版)

基　　金：supported in part by the National Natural Science Foundation of China(No.62221005);the National Key Research and Development Program of China(No.2021YFF0704101,No.2020YFC2003502);the National Natural Science Foundation of China(No.61876201);the Natural Science Foundation of Chongqing(No.cstc2019jcyj-cxtt X0002,No.cstc2021ycjh-bgzxm0013);the key cooperation project of chongqing municipal education commission(HZ2021008)。

摘　　要：Causality extraction has become a crucial task in natural language processing and knowledge graph.However,most existing methods divide causality extraction into two subtasks:extraction of candidate causal pairs and classification of causality.These methods result in cascading errors and the loss of associated contextual information.Therefore,in this study,based on graph theory,an End-to-end Multi-Granulation Causality Extraction model(EMGCE)is proposed to extract explicit causality and directly mine implicit causality.First,the sentences are represented on different granulation layers,that contain character,word,and contextual string layers.The word layer is fine-grained into three layers:word-index,word-embedding and word-position-embedding layers.Then,a granular causality tree of dataset is built based on the word-index layer.Next,an improved tagREtriplet algorithm is designed to obtain the labeled causality based on the granular causality tree.It can transform the task into a sequence labeling task.Subsequently,the multi-granulation semantic representation is fed into the neural network model to extract causality.Finally,based on the extended public SemEval 2010 Task 8 dataset,the experimental results demonstrate that EMGCE is effective.

关 键 词：Causality extraction Granular computing Granular causality tree Semantic representation Sequence labeling 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]