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. 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.
latent semantic analysis knowledge representation cognitive science