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ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
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CNKI
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Impact Factor 2022: 1.0
3.1
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-03-28
Vol. 15, No. 3 has been published online!
2024-02-26
The papers published in Vol. 15, Nos. 1&2 have been registered with Crossref.
2024-02-26
Vol. 15, No. 2 has been published online!
Home
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2020
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Volume 11, No. 2, May 2020
>
Causal Web Determination from Texts
Chaveevan Pechsiri
1
, Narongdech Keeratipranon
1
, and Intaka Piriyakul
2
1. College of Innovative Technology and Engineering, Dhurakij Pundit University, Bangkok, Thailand
2. Faculty of Social Sciences, Srinakharinwirot University, Bangkok, Thailand
Abstract
—The research aim is to determine a causal web from downloaded guru web-board documents. The causal web which benefits a diagnosis service assistant of a problem-solving system consists of several cause-effect pair sequences where each cause-effect pair has a cause-effect relation and the last cause-effect pair of each cause-effect pair sequence has the same effect concept. Each causative/effect concept is expressed by an elementary discourse unit or a simple sentence. The research has three problems; how to determine the cause-effect pair with an overlap problem between a causative-verb concept set and an effect-verb concept set, how to determine cause-effect pair sequences including causative/effect boundary determination, and how to determine the causal web on the extracted cause-effect pair sequences without redundant sequences. We use a word co-occurrence to represent a sentence’s event/state with a causative/effect concept. We then propose using a self-Cartesian product on a collected word co-occurrence set and Naïve Bayes including categorized verb groups to extract each cause-effect pair sequence including the boundary determination without the verb-concept-overlap influence. And we use a dynamic template matching technique to determine the causal web without the redundancy. The research result has a high percentage correctness of the causal web determination.
Index Terms
—word co-occurrence, elementary discourse unit, template matching
Cite: Chaveevan Pechsiri, Narongdech Keeratipranon, and Intaka Piriyakul, "Causal Web Determination from Texts," Journal of Advances in Information Technology, Vol. 11, No. 2, pp. 64-70, May 2020. doi: 10.12720/jait.11.2.64-70
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
4-CE01-Thailand
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