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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/108746

    题名: A Study on Machine Learning for Imbalanced Datasets with Answer Validation of Question Answering
    作者: Day, Min-Yuh;Tsai, Cheng-Chia
    关键词: Answer Validation;Imbalanced Datasets;Machine Learning;Question Answering;QA-Lab;Support Vector Machine
    日期: 2016/07/30
    上传时间: 2016-12-07 02:10:36 (UTC+8)
    出版者: IEEE
    摘要: 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.
    關聯: Proceedings of the 2016 IEEE 17th International Conference on Information Reuse and Integration (IEEE IRI 2016)
    显示于类别:[資訊管理學系暨研究所] 會議論文


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