基于潜在语义分析和筛选算法的日语短文答案自动评分系统比较性能

A. A. P. Ratna, Lea Santiar, Ihsan Ibrahim, Prima Dewi Purnamasari, Dyah Lalita Luhurkinanti, Adisa Larasati
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引用次数: 4

摘要

本文对电子学习在短文评分系统中的应用进行了研究。本系统是根据日语学习项目的短文考试需要时间和精力来完成这些任务的需要而开发的。人的能力受到能量的限制,认知评估的客观性会随着时间的流逝而降低。印尼大学电子工程系在开发短文答案自动评分系统SIMPLE-O时,采用了潜在语义分析(LSA)和筛选算法两种方法。这两种算法的选择是基于其在不需要了解其语言特征的情况下进行语义分析的能力。LSA的主要方法是奇异值分解(SVD),另外基于指纹识别的窗化算法。将这些算法应用于日语考试自动评估系统中,结果接近,其中Winnowing算法的平均准确率仅比LSA低1.06%,而LSA的平均准确率为87.78%。这两种算法应该适用于日语短文答案的评分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Latent Semantic Analysis and Winnowing Algorithm Based Automatic Japanese Short Essay Answer Grading System Comparative Performance
In this paper, advanced of research on e-learning application for short essay grading system had been conducted. This system was developed based on the needs of Japanese Language study program for short essay examination that required time and focus for finishing those tasks. Human abilities are limited by their energy, so that cognitive assessment objectivity could decrease in line with the elapsed time. Latent Semantic Analysis (LSA) and Winnowing Algorithm are two methods used in developing the automatic short essay answer grading system called SIMPLE-O by Department of Electrical Engineering, Universitas Indonesia. These two algorithms are chosen based on its ability to do semantic analytic without the needs of understanding about the characteristic of its languages. LSA used Singular Value Decomposition (SVD) as its main method, besides Winnowing algorithm is based on fingerprinting. These algorithms are applied into the automatic system to assess the Japanese language exam with close results between them with average accuracy of Winnowing algorithm is only 1.06% lower than LSA that could gain 87.78%. These two algorithms should be suitable for grading short essay answer in Japanese language.
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