Essay Assessment with Latent Semantic Analysis
Essay Assessment with Latent Semantic Analysis
复制标题
通过潜在语义分析进行论文评估
DOI：
10.2190/w5ar-dypw-40kx-fl99
复制
发表时间：
2003
影响因子：
4.8
通讯作者：
Tristan Miller
中科院分区：
文献类型：
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作者：
Tristan Miller
潜在语义分析（LSA）是一种自动化的统计技术，用于比较单词或文档的语义相似性。在这篇文章中，我将研究LSA在自动作文评分中的应用。我比较LSA方法与早期的统计方法评估论文质量，并批判性地审查当代的论文评分系统建立在LSA，包括智能论文评估，总结街，国家的本质，顶点，并选择一个Kibitzer。最后，我讨论了目前的研究途径，包括LSA的应用程序，计算机测量的可读性评估和自动摘要的学生论文。
Latent semantic analysis (LSA) is an automated, statistical technique for comparing the semantic similarity of words or documents. In this article, I examine the application of LSA to automated essay scoring. I compare LSA methods to earlier statistical methods for assessing essay quality, and critically review contemporary essay-scoring systems built on LSA, including the Intelligent Essay Assessor, Summary Street, State the Essence, Apex, and Select-a-Kibitzer. Finally, I discuss current avenues of research, including LSA's application to computer-measured readability assessment and to automatic summarization of student essays.