A Study on Machine Learning for Imbalanced Datasets with Answer Validation of Question Answering

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Title:	A Study on Machine Learning for Imbalanced Datasets with Answer Validation of Question Answering
Authors:	Day, Min-Yuh;Tsai, Cheng-Chia
Keywords:	Answer Validation;Imbalanced Datasets;Machine Learning;Question Answering;QA-Lab;Support Vector Machine
Date:	2016-07-30
Issue Date:	2016-12-07 02:10:36 (UTC+8)
Publisher:	IEEE
Abstract:	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 dataset. As a result, our system achieved 90% accuracy with imbalanced datasets machine learning model for the NTCIR-12 QA-Lab2 development datasets.
Relation:	Proceedings of the 2016 IEEE 17th International Conference on Information Reuse and Integration (IEEE IRI 2016)
Appears in Collections:	[Graduate Institute & Department of Information Management] Proceeding

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