A Study on Machine Learning for Imbalanced Datasets with Answer Validation of Question Answering
Min-Yuh Day, Cheng-Chia Tsai · 2016
Question Answering is a system that can process and answer a given question. In recent years, an enormous number of studies have been made on question answering, little is known about the effects of imbalanced datasets with answer validation of question answer system. The objective of this paper is to provide a better understanding of the effects of imbalanced datasets model for answer validation in a real world university entrance exam question answering system. In this paper, we proposed a question answer system and provided a comprehensive analysis of imbalanced datasets and balanced datasets model with Answer Validation of Question Answering system using NTCIR-12 QA-Lab2 Japanese university entrance exams English translation development and test datasets. As a result, our system achieved 90% accuracy with imbalanced datasets machine learning model for the NTCIR-12 QA-Lab2 development datasets.