Extraction of explanation based Symptom-Treatment relation from texts

This paper aims to extract the explanation-based problem-solving relation,especially the symptom-treatment relation,from hospital-web-board documents. the extracted relations benefit people who are learning how to solve their health problems. the research includes three main problems: 1) how to identify symptom-concept edus (where an edu is an elementary discourse unit or a simple sentence/clause) and treatment concept edus,2) how to identify the symptomconcept-edu boundary and the treatment-concept-edu boundary as an explanation,3) how to determine symptom-treatment relations from documents. therefore,we propose collecting each multi-word-co occurrence with either a symptom concept or a treatment concept from a verb-phrase to identify each symptom-concept edu and each treatment-concept edu including their boundaries. collecting multi-word-co involves two more problems of the ambiguous multi-word-co and the multi-word-co size. thus,we apply the bayesian network to solve both problems of multi-word-co after applying word rules. the symptom-treatment relation can be solved by naive bayes learning vector pairs of symptom vectors and treatment vectors. the research results can provide high precision when extracting symptom-treatment relations through texts.