Research Article Improving Causality Induction with Category Learning

permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 systemwith 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 o