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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2017 Vol.7(5): 94-99 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2017.7.5.627

Chain of Causation Determination from Texts

Chaveevan Pechsiri and Renu Sukharomana
Abstract—This research aims to determine the chain of causation of problem events, especially drug-addiction, expressed by several simple sentences from web documents. The chain of causation determination benefits for the problem-solving system. The research has three problems; how to determine a sentence having causative/effect event concept, how to determine the causative/effect event-concept vector size, and how to determine several consecutive causality relations (each causality is a relation between a causative-event-concept vector and an effect-event-concept vector) occurring as the chain of causation. Therefore, we apply WordCo to solve the cause/effect event concepts. We also use Support Vector Machine and WordCo features to solve the causative-event/effect-event vector size/boundary. We then propose using Naïve Bayes to determine the consecutive causality relations between causative event-concept vectors and effect event-concept vectors. The research results provide the high precision of the chain of causation determination from the documents.

Index Terms—Chain of causation, effect boundary, elementary discourse unit, WordCo.

Chaveevan Pechsiri is with the College of Innovative of Technology and Engineering, Dhurakij Pundit University, Bangkok, Thailand (e-mail: chaveevan.pec@dpu.ac.th).
Renu Sukharomana was with Dhurakij Pundit University, Bangkok, Thailand. She is now with the Institute of Social and Economic Science, Thailand Research Fund, Bangkok, Thailand (e-mail: sukharenu@gmail.com).

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Cite: Chaveevan Pechsiri and Renu Sukharomana, "Chain of Causation Determination from Texts," International Journal of Machine Learning and Computing vol. 7, no. 5, pp. 94-99, 2017.

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