Web Board Question Answering System on Problem-Solving Through Problem Clusters

摘要

This paper aims to work on the Question Answering (QA) system within online web boards, especially the Why-question, How-question, and Request-Diagnosis-question types approach for solving problems. The research QA system benefits for the online communities in solving their problems, especially on health-care problems of symptoms. Both question and answer expressions are based on multiple EDUs (Elementary Discourse Units) where each EDU is equivalent to a simple sentence or a clause. The research involves two main problems: how to identify the question types of Why, How, and Request-Diagnosis and how to determine the corresponding answer from the knowledge source after solving the question focuses. Thus, the research applies different machine learning techniques, Na?ve Bayes and Support Vector Machine, to solve the reasoning question type identification. The knowledge source contains several symptom-treatment vector pairs and several cause-effect vector pairs. Therefore, we propose clustering symptoms/problems of the knowledge source before determining an answer based on top-down levels of determining similarity scores between a web board question and the knowledge source. The research achieves 83 % correctness of the answer determination with potentially saving amounts of search time.