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首页> 外文会议>2017 15th International Conference on Quality in Research : International Symposium on Electrical and Computer Engineering >Word level auto-correction for latent semantic analysis based essay grading system
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Word level auto-correction for latent semantic analysis based essay grading system

机译：基于潜在语义分析的论文评分系统的词级自动校正

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摘要

Assessment is an important step in the learning process in which the assessor evaluates students' level of understanding. One model of assessment is essay, which may cause problems in scoring objectivity and performance drop of human body when grading many essays. To ease essay grading and resolve those problems, a system that can assess documents according to its contexts is needed. From this concern, we developed a Java-based system for grading essays in Indonesian language using a more efficient and optimal algorithm. This algorithm consisted of 4 stages. The first stage is Latent Semantic Analysis (LSA), which is used to obtain and conclude the contextual relation of words meaning in a text. The second stage uses Single Value Decomposition (SVD) to obtain scatter variance from the relations. SVD identifies where variances appear at most, therefore is enabled to find the best approach to the original data using reduced dimensions. The third stage is Latent Semantic Indexing (LSI) which is an indexing and retrieval method to identifies patterns in relation between terms and concepts contained in unstructured text collection and results with a vector representing the text. The last stage is Cosine Similarity Measurement (CSM) to obtain similarity value from the text and answer document. To resolve problems stemmed from grammar and vocabulary, in this work we propose an auto-correction technique to check a word from word library for equalization of word with same or no specific meaning. Then, Jaro-Winkler distance algorithm is used to check word errors caused by accident when typing. With the distance, we can determine whether two strings of word are similar. This is extremely important when scanning text with typos, as it will affect the result from LSA. Using this system, the value obtained is similar to the value obtained from human rater. With word library consisting of 97 words for synonym check and 204 function words, the resulting accuracy is 85.246% ± 13.129.
机译：评估是学习过程中的重要一步，评估者在此过程中评估学生的理解水平。一种评估模型是作文，在对许多作文进行评分时，可能会在评分客观性和人体性能下降方面引起问题。为了简化论文评分并解决这些问题，需要一种能够根据上下文评估文档的系统。因此，我们开发了一种基于Java的系统，该系统使用更高效，更优化的算法对印度尼西亚语的论文进行评分。该算法包括四个阶段。第一阶段是潜在语义分析（LSA），它用于获取和总结文本中单词含义的上下文关系。第二阶段使用单值分解（SVD）从关系中获得分散方差。 SVD可以识别出最多出现方差的位置，因此可以使用缩小的维找到原始数据的最佳方法。第三阶段是潜在语义索引（LSI），这是一种索引和检索方法，用于识别非结构化文本集合中包含的术语和概念之间的关系模式以及带有表示文本的向量的结果。最后一步是余弦相似度测量（CSM），用于从文本和答案文档中获取相似度值。为了解决源自语法和词汇的问题，在这项工作中，我们提出了一种自动更正技术，用于检查单词库中的单词以使具有相同或没有特定含义的单词相等。然后，使用Jaro-Winkler距离算法检查打字时由于意外引起的单词错误。通过距离，我们可以确定两个单词串是否相似。当扫描带有错字的文本时，这非常重要，因为它将影响LSA的结果。使用该系统，获得的值类似于从人类评估者获得的值。使用由97个用于同义词检查的单词和204个功能词组成的单词库，结果精度为85.246％±13.129。

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