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从文本中自动推导读者的知识结构。

Automatically deriving readers' knowledge structures from texts.

作者信息

Foltz P W, Wells A D

机构信息

Department of Psychology, New Mexico State University, Las Cruces 88003, USA.

出版信息

Behav Res Methods Instrum Comput. 1999 May;31(2):208-14. doi: 10.3758/bf03207712.

Abstract

Latent semantic analysis (LSA) serves as both a theory and a method for representing the meaning of words based on a statistical analysis of their contextual usage (Foltz, 1996; Landauer & Dumais, 1997). In experiments in the domains of psychology and history, we compared the representation of readers' knowledge structures of information learned from texts with the representation generated by LSA. Results indicated that LSA's representation is similar to readers' representations. In addition, the degree to which the reader's representation is similar to LSA's representation is indicative of the amount of knowledge the reader has acquired and of the reader's reading ability. This approach has implications both as a model of learning from text and as a practical tool for performing knowledge assessment.

摘要

潜在语义分析(LSA)既是一种理论,也是一种基于对单词上下文用法的统计分析来表示单词含义的方法(福尔茨,1996;兰道尔和杜梅斯,1997)。在心理学和历史领域的实验中,我们将读者从文本中学到的信息的知识结构表示与LSA生成的表示进行了比较。结果表明,LSA的表示与读者的表示相似。此外,读者的表示与LSA的表示的相似程度表明了读者获得的知识量和读者的阅读能力。这种方法作为一种从文本学习的模型和一种进行知识评估的实用工具都具有重要意义。

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