Improving Causality Induction with Category Learning
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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/650147 |
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| author | Yi Guo Zhihong Wang Zhiqing Shao |
| author_facet | Yi Guo Zhihong Wang Zhiqing Shao |
| author_sort | Yi Guo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e9e5d2f0c99c4818bee5016759117bd0 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e9e5d2f0c99c4818bee5016759117bd02025-08-20T02:21:53ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/650147650147Improving Causality Induction with Category LearningYi Guo0Zhihong Wang1Zhiqing Shao2Department of Computer Science and Engineering, East China University of Science and Technology, P.O. Box 408, Shanghai 200237, ChinaDepartment of Computer Science and Engineering, East China University of Science and Technology, P.O. Box 408, Shanghai 200237, ChinaDepartment of Computer Science and Engineering, East China University of Science and Technology, P.O. Box 408, Shanghai 200237, ChinaCausal 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.http://dx.doi.org/10.1155/2014/650147 |
| spellingShingle | Yi Guo Zhihong Wang Zhiqing Shao Improving Causality Induction with Category Learning The Scientific World Journal |
| title | Improving Causality Induction with Category Learning |
| title_full | Improving Causality Induction with Category Learning |
| title_fullStr | Improving Causality Induction with Category Learning |
| title_full_unstemmed | Improving Causality Induction with Category Learning |
| title_short | Improving Causality Induction with Category Learning |
| title_sort | improving causality induction with category learning |
| url | http://dx.doi.org/10.1155/2014/650147 |
| work_keys_str_mv | AT yiguo improvingcausalityinductionwithcategorylearning AT zhihongwang improvingcausalityinductionwithcategorylearning AT zhiqingshao improvingcausalityinductionwithcategorylearning |