The potential of latent semantic analysis for machine grading of clinical case summaries
The potential of latent semantic analysis for machine grading of clinical case summaries
复制标题
DOI：
10.1016/s1532-0464(02)00004-7
复制
发表时间：
2002-02-01
影响因子：
4.5
通讯作者：
Kintsch, W
中科院分区：
文献类型：
--
作者：
Kintsch, W
目的：介绍潜在语义分析(LSA)，这是一种用于表示词、句子和文本的意义的机器学习方法。LSA从阅读大量文本中诱导出一个高维语义空间。单词和文本的意义可以在这个空间中表示为向量，因此可以自动和客观地进行比较。心理理论：描述了一种基于LSA的心理词汇生成理论。单词向量LSA构造是上下文无关的，并且每个单词，无论它具有多少含义或意义，都由单个向量表示。然而，当一个词在不同的语境中使用时，就会出现与语境相适应的词义。目前的应用：描述了LSA在教育软件中的几个应用，涉及LSA快速比较文本内容的能力，例如学生写的一篇文章和目标文章。潜在的医疗应用：描述了一个基于LSA的软件工具，用于对医学生撰写的临床病例总结进行机器评分。(C)2002年埃尔塞维尔科学公司(美国)。版权所有。
`Objective: This paper introduces latent semantic analysis (LSA), a machine learning method for representing the meaning of words, sentences, and texts. LSA induces a high-dimensional semantic space from reading a very large amount of texts. The meaning of words and texts can be represented as vectors in this space and hence can be compared automatically and objectively. Psychological theory: A generative theory of the mental lexicon based on LSA is described. The word vectors LSA constructs are context free, and each word, irrespective of how many meanings or senses it has, is represented by a single vector. However, when a word is used in different contexts, context appropriate word senses emerge. Current applications: Several applications of LSA to educational software are described, involving the ability of LSA to quickly compare the content of texts, such as an essay written by a student and a target essay. Potential medical applications: An LSA-based software tool is sketched for machine grading of clinical case summaries written by medical students. (C) 2002 Elsevier Science (USA). All rights reserved.