A Study on Machine Learning for Imbalanced Datasets with Answer Validation of Question Answering
學年 104
學期 2
發表日期 2016-07-28
作品名稱 A Study on Machine Learning for Imbalanced Datasets with Answer Validation of Question Answering
作品名稱（其他語言）
著者 Min-Yuh Day; Cheng-Chia Tsai
作品所屬單位
出版者
會議名稱 2016 IEEE 17th International Conference on Information Reuse and Integration (IEEE IRI 2016)
會議地點 Pittsburgh, Pennsylvania, USA
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.
關鍵字 Answer Validation;Imbalanced Datasets;Machine Learning;Question Answering;QA-Lab;Support Vector Machine
語言 en_US
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20160728~20160730
通訊作者
國別 USA
公開徵稿
出版型式
出處 Proceedings of the 2016 IEEE 17th International Conference on Information Reuse and Integration (IEEE IRI 2016)
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/108746 )