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 cl...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.展开更多
Urban areas have many problems,including homelessness,graffiti,and littering.These problems are influenced by various factors and are linked to each other;thus,an understanding of the problem structure is required in ...Urban areas have many problems,including homelessness,graffiti,and littering.These problems are influenced by various factors and are linked to each other;thus,an understanding of the problem structure is required in order to detect and solve the root problems that generate vicious cycles.Moreover,before implementing action plans to solve these problems,local governments need to estimate cost-effectiveness when the plans are carried out.Therefore,this paper proposed constructing an urban problem knowledge graph that would include urban problems’causality and the related cost information in budget sheets.In addition,this paper proposed a method for detecting vicious cycles of urban problems using SPARQL queries with inference rules from the knowledge graph.Finally,several root problems that led to vicious cycles were detected.Urban-problem experts evaluated the extracted causal relations.展开更多
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)+2 种基金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)。
文摘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.
基金supported by Japan Society for the Promotion of Science(JSPS)KAKENHI(No.16K12411,No.16K00419,No.16K12533,No.17H04705,and No.18J13988)
文摘Urban areas have many problems,including homelessness,graffiti,and littering.These problems are influenced by various factors and are linked to each other;thus,an understanding of the problem structure is required in order to detect and solve the root problems that generate vicious cycles.Moreover,before implementing action plans to solve these problems,local governments need to estimate cost-effectiveness when the plans are carried out.Therefore,this paper proposed constructing an urban problem knowledge graph that would include urban problems’causality and the related cost information in budget sheets.In addition,this paper proposed a method for detecting vicious cycles of urban problems using SPARQL queries with inference rules from the knowledge graph.Finally,several root problems that led to vicious cycles were detected.Urban-problem experts evaluated the extracted causal relations.
