Extraction of cause-effect-concept pair series from web documents

This research aims to extract a cause-effect-concept pair series of consequent event occurrences in health information of hospital web-boards. The extracted cause-effect-concept pair series representing a disease causation pathway benefits for the automatic diagnosis and solving system. Where each causative/effect event concept is expressed by an elementary discourse unit (EDU which is a simple sentence). The research has three problems; how to determine causative/effect concept EDUs from the documents containing some EDU occurrences with both causative concepts and effect concepts, how to determine the cause-effect relation between two adjacent EDUs having the discourse cue ambiguity, and how to extract cause-effect-concept pair series mingled with either a stimulation relation EDU or other non-cause-effect relation EDUs from the documents. Therefore, we apply annotated NWordCo pairs with causative-effect concepts to represent EDU pairs with causative-effect concept where the NWordCo size solved by Naïve Bayes. We also apply Naïve Bayes to solve NWordCo-concept pairs having the cause-effect relation from the adjacent EDU pairs. We then propose using cue words and the collected NWordCoconcept pairs with the cause-effect relation to extract the cause-effectconcept pair series. The research results provide the high precision of the cause-effect-concept pair series determination from the documents.