Extraction of a group pair relation: problem solving relation from web board documents
This paper aims to extract a group pair relation as a Problem Solving relation, for example a DiseaseSymptom Treatment relation and a CarProblem Repair relation, between two event explanation groups, a problem concept group as a symptom/CarProblem concept group and a solving concept group as a treatment concept/repair concept group from hospital web board and car repair guru web board documents. The Problem Solving relation (particularly Symptom Treatment relation) including the graphical representation benefits non professional persons by supporting knowledge of primarily solving problems. The research contains three problems: how to identify an EDU (an Elementary Discourse Unit, which is a simple sentence) with the event concept of either a problem or a solution; how to determine a problem concept EDU boundary and a solving concept EDU boundary as two event explanation groups, and how to determine the Problem Solving relation between these two event explanation groups. Therefore, we apply word co occurrence to identify a problem concept EDU and a solving concept EDU, and machine learning techniques to solve a problem concept EDU boundary and a solving concept EDU boundary. We propose using k mean and Naive Bayes to determine the Problem Solving relation between the two event explanation groups involved with clustering features. In contrast to previous works, the proposed approach enables group pair relation extraction with high accuracy.
SpringerPlus