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

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