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Causal Pathway Extraction from Web-Board Documents

College of Innovative Technology and Engineering, Dhurakij Pundit University, Bangkok 10210, Thailand
Department of Computer Science, Ramkhamhaeng University, Bangkok 10240, Thailand
Author to whom correspondence should be addressed.
Academic Editors: Arturo Montejo-Ráez and Salud María Jiménez-Zafra
Appl. Sci. 2021, 11(21), 10342;
Received: 14 September 2021 / Revised: 28 October 2021 / Accepted: 28 October 2021 / Published: 3 November 2021
(This article belongs to the Special Issue Current Approaches and Applications in Natural Language Processing)
This research aim is to extract causal pathways, particularly disease causal pathways, through cause-effect relation (CErel) extraction from web-board documents. The causal pathways benefit people with a comprehensible representation approach to disease complication. A causative/effect-concept expression is based on a verb phrase of an elementary discourse unit (EDU) or a simple sentence. The research has three main problems; how to determine CErel on an EDU-concept pair containing both causative and effect concepts in one EDU, how to extract causal pathways from EDU-concept pairs having CErel and how to indicate and represent implicit effect/causative-concept EDUs as implicit mediators with comprehension on extracted causal pathways. Therefore, we apply EDU’s word co-occurrence concept (wrdCoc) as an EDU-concept and the self-Cartesian product of a wrdCoc set from the documents for extracting wrdCoc pairs having CErel into a wrdCoc-pair set from the documents after learning CErel on wrdCoc pairs by supervised-machine learning. The wrdCoc-pair set is used for extracting the causal pathways by wrdCoc-pair matching through the documents. We then propose transitive closure and a dynamic template to indicate and represent the implicit mediators with the explicit ones. In contrast to previous works, the proposed approach enables causal-pathway extraction with high accuracy from the documents. View Full-Text
Keywords: cause-effect relation; transitive closure; word co-occurrence cause-effect relation; transitive closure; word co-occurrence
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MDPI and ACS Style

Pechsiri, C.; Piriyakul, R. Causal Pathway Extraction from Web-Board Documents. Appl. Sci. 2021, 11, 10342.

AMA Style

Pechsiri C, Piriyakul R. Causal Pathway Extraction from Web-Board Documents. Applied Sciences. 2021; 11(21):10342.

Chicago/Turabian Style

Pechsiri, Chaveevan, and Rapepun Piriyakul. 2021. "Causal Pathway Extraction from Web-Board Documents" Applied Sciences 11, no. 21: 10342.

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