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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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Detalles Bibliográficos
Autores principales: Guo, Yi, Wang, Zhihong, Shao, Zhiqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4032716/
https://www.ncbi.nlm.nih.gov/pubmed/24883419
http://dx.doi.org/10.1155/2014/650147
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author Guo, Yi
Wang, Zhihong
Shao, Zhiqing
author_facet Guo, Yi
Wang, Zhihong
Shao, Zhiqing
author_sort Guo, Yi
collection PubMed
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.
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spelling pubmed-40327162014-06-01 Improving Causality Induction with Category Learning Guo, Yi Wang, Zhihong Shao, Zhiqing ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2014 2014-04-30 /pmc/articles/PMC4032716/ /pubmed/24883419 http://dx.doi.org/10.1155/2014/650147 Text en Copyright © 2014 Yi Guo et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Guo, Yi
Wang, Zhihong
Shao, Zhiqing
Improving Causality Induction with Category Learning
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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4032716/
https://www.ncbi.nlm.nih.gov/pubmed/24883419
http://dx.doi.org/10.1155/2014/650147
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