title: Improving causality induction with category learning.
abstract: Causal relations are of fundamental importance for human perception and reasoning. According to the nature of causality, causality has explicit and implicit forms. In the case of explicit form, causal-effect relations exist at either clausal or discourse levels. The implicit causal-effect relations heavily rely on empirical analysis and evidence accumulation. This paper proposes a comprehensive causality extraction system (CL-CIS) integrated with the means of category-learning. CL-CIS considers cause-effect relations in both explicit and implicit forms and especially practices the relation between category and causality in computation. In elaborately designed experiments, CL-CIS is evaluated together with general causality analysis system (GCAS) and general causality analysis system with learning (GCAS-L), and it testified to its own capability and performance in construction of cause-effect relations. This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning.
authors.affiliation: Department of Computer Science and Engineering, East China University of Science and Technology, P.O. Box 408, Shanghai 200237, China ; School of Information Science and Technology, Shihezi University, Shihezi 832003, China.
authors.forename: Yi
authors.identifier: 0000-0002-6088-7198
authors.initials: Y
authors.lastname: Guo
authors.affiliation: Department of Computer Science and Engineering, East China University of Science and Technology, P.O. Box 408, Shanghai 200237, China.
authors.forename: Zhihong
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Engineering, East China University of Science and Technology, P.O. Box 408, Shanghai 200237, China.
authors.forename: Zhiqing
authors.initials: Z
authors.lastname: Shao
mesh_terms: D001245:Association Learning; D015984:Causality; D003210:Concept Formation; D006801:Humans; D007802:Language; D007858:Learning; D008960:Models, Psychological