Research On Emergency Causality Extraction From Chinese Corpus

As Emergency is a complex system, important task of its qualitative modeling is to analyze causality among various elements within Emergency system, which is the basis work for establishing simulation model or prediction model of emergencies. However, the expert-based acquiring causality methods in modeling of emergency are questionnaire and interview, which there is a time-consuming, labor-intensive and poor operational limitation. And data-based Method depends on a certain scale and complete data samples, while there are no data accumulation, lack of integrity and continuity of data in many emergency areas.But with emergency management mechanism construction in China and academic research continued to deepen, a huge amount of text resources about the emergency are accumulated. These texts contains a lot of emergency evolution law knowledge, especially reflects casuality among factors in emergeny system, which is named emergency cauality. These texts can replace experts and data as the emergency causal knowledge sources. So how to extract causal knowledge of internal elements in emergency from text, and establish emergency causality model is needed to be solved urgently. Extraction causality method from Chinese corpus has been little studied and efficient integration approach of causality extracted from Chinese corpus was not found. Therefore, Method of causality extraction from Chinese corpus and integration of emergency causality extracted from multiple texts were research in this dessertation based on previous accumulated domestic and foreign research results. Researches have been done are as follow in the dissertation.(1) Emergeny causal model have been built. Firstly, common characters of emergency were learned based on analysis of emergeny. Secondly, Emergency causal model was built using system engineering method, and causality in Emergency causal model was analyzed.Its innovation is that scalable emergency causality model are built based on common element of emergency system's inputs, states and outputs, and causality among elements in emergency and causal relation between emergency are clear, which can provide a representation model for cauality extracted from text.(2) Causal syntactic patterns in Chinese were discoved and method of explicit causality extraction was research in the Chinese corpus. Explicit causality is an important source of causality in emergency text, and method of extraction causality from Chinese corpus has little researched. Firstly, Chinese sentences were divided into explicit causal sentences and ambiguity causal sentences, and five kinds of Causal syntactic patterns were discoved based on Chinese grammar, and matching rules of causal sentence and match method of Causal syntactic patterns were proposed. And then, ambiguity causal setences classification model was researched based on Naive Bayes in the Chinese corpus. Finally, method of causality extraction was proposed and a good result was achieved in experiments. Its innovation is that five kinds of causal syntactic patterns were discoved, and basic expressions of causal sentence in the Chinese were discovered, and computer aided Chinese causality extraction theory were improved. Causal syntactic patterns based method of explicit causality extraction was porposed to solved the limit of traditional method not distinguish ambiguity causal sentences.(3) Method of implicit causality extraction from Chinese corpus was researched. Implicit causality is also another important source of causality in emergency text. Based on analysis of charater of Chinese corpus implicit causality in emergency field, implicit causality extraction method was researched by concept entity. First frequent concept set was generated from concepts in preprocessed sentences, and then causality of frequent concept set was analysed. Finally causal and effect were judged. Its innovation is that implicit causality extraction method proposed is a method integrating with linguistics and causal theory from philosophy and statistics. Based on causal theory of Hume and Suppes, calculation of confidence in method of association analysis has been improved. Design of causality evaluation function and causal component judgment method was considerated from time of priority, causal probabilistic and causal dependence.which solved the problem method of association analysis is not fitted in cauality mining from text. The method of implicit causality extraction proposed is a novel method based on casual theory.(4) Integration method of redundancy, conflict and sparse emergency causality was researched. Casual relation extracted from text may be redundancy, conflict and sparse, to solve the problem integration method of individual causal cognition from one text was researched to global causal cognition of emergency. Firstly, based on the vector space model field texts select method was researched. And then D-S evidence theory based integration method of causality extrated from multiple texts was studied. Its innovation is that D-S evidence theory based integration method of causality extrated from multiple texts was proposed, and problem of sparse, conflict and redundant causalitys was eliminated. Emergency causality model generated by method proposed can truly reflect internal causality among elements of emergency to overcome deficiencies of single text for emergency described. Generation of bayesian network structure for emergency provides a new method based on text mining. On one hand common casuality cognition of emergency was achieved by elimination of conflict and redundant causalitys, on the other hand overall casuality cognition of emergency was achieved by complementary of causality.