Automated essay scoring (AES) is a vital area of research aiming to provide efficient and accurate assessment tools for evaluating written content. This study investigates the effectiveness of two popular similarity metrics, Jaccard coefficient, and Cosine similarity, within the context of vector space models(VSM)employing unigram, bigram, and trigram representations. The data used in this research was obtained from the formative essay of the citizenship education subject in a junior high school. Each essay undergoes preprocessing to extract features using n-gram models, followed by vectorization to transform text data into numerical representations. Then, similarity scores are computed between essays using both Jaccard coefficient and Cosine similarity. The performance of the system is evaluated by analyzing the root mean square error (RMSE), which measures the difference between the scores given by human graders and those generated by the system. The result shows that the Cosine similarity outperformed the Jaccard coefficient. In terms of n-gram, unigrams have lower RMSE compared to bigrams and trigrams.

翻译：自动作文评分（AES）是一个重要的研究领域，旨在为书面内容评估提供高效准确的评分工具。本研究探讨了在采用单字、双字和三字表示的向量空间模型（VSM）中，两种常用相似度度量——Jaccard系数与余弦相似度的有效性。研究数据来源于初中公民教育科目的形成性作文。每篇作文经过预处理后，使用n-gram模型提取特征，随后通过向量化将文本数据转换为数值表示。接着，分别使用Jaccard系数和余弦相似度计算作文间的相似度分数。系统性能通过分析均方根误差（RMSE）进行评估，该指标衡量了人工评分者给出的分数与系统生成分数之间的差异。结果表明，余弦相似度的表现优于Jaccard系数。在n-gram方面，单字模型的RMSE低于双字和三字模型。