文本挖掘中的自动简答评分(ASAG),余弦相似度、Jaccard相似度和骰子系数三种方法的比较

Tri Wahyuningsih, Henderi Henderi, W. Winarno
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引用次数: 11

摘要

本研究旨在通过提供一个参考答案,比较余弦相似度、Jaccard相似度和骰子系数三种方法,寻找自动简答评分(ASAG)的相关性评估。从Python编程语言计算和电子表格数据处理结果来看,Dice Coefficient法的相关平均值最高,为0.76,Cosine Similarity法次之,平均相关值为0.76,Jaccard法的相关平均值最低,为0.69。本研究的贡献在于在一个数据中使用了三种方法,而之前的研究仅使用一种方法用于一个数据或两种方法用于一个数据。因此,本研究的价值使得数据的比较更加完整和准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Text Mining an Automatic Short Answer Grading (ASAG), Comparison of Three Methods of Cosine Similarity, Jaccard Similarity and Dice's Coefficient
This study aims to find correlation assessment of Automatic Short Answer Grading (ASAG) by comparing three methods of Cosine Similarity, Jaccard Similarity and Dice Coefficient by providing one reference answer. From the results of computing using Python programming language and data processing using spreadsheets, it was obtained that the Dice Coefficient method had the highest correlation average value of 0.76, followed by Cosine Similarity with an average correlation value of 0.76, and the lowest correlation average value was the Jaccard method with a value of 0.69. The contribution to this study is the use of three methods in one data, whereas the previous research only used 1 method for 1 data or 2 methods for 1 data. So, the value in this study resulted in a more complete comparison and accuracy of data.
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