The potential of latent semantic analysis for machine grading of clinical case summaries.
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.
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.
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.
An LSA-based software tool is sketched for machine grading of clinical case summaries written by medical students.
Kintsch
Walter
W
Institute of Cognitive Science, University of Colorado, USA. wkintsch@psych.colorado.edu
D001185:Artificial Intelligence; D019295:Computational Biology; D003194:Computer-Assisted Instruction; D004504:Education, Medical, Undergraduate; D006801:Humans; D012660:Semantics; D012984:Software