A revised algorithm for latent semantic analysis

Authors:
Xiangen Hu;Zhiqiang Cai;Max Louwerse;Andrew Olney;Phanni Penumatsa;Art Graesser
Affiliations:
Department of Psychology, The University of Memphis, Memphis, TN;Department of Psychology, The University of Memphis, Memphis, TN;Department of Psychology, The University of Memphis, Memphis, TN;Department of Psychology, The University of Memphis, Memphis, TN;Department of Psychology, The University of Memphis, Memphis, TN;Department of Psychology, The University of Memphis, Memphis, TN
Venue:
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Year:
2003

Citing 2
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Learning human-like knowledge by singular value decomposition: a progress report

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
AutoTutor: A simulation of a human tutor

Cognitive Systems Research

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Abstract

The intelligent tutoring system AutoTutor uses latent semantic analysis to evaluate student answers to the tutor's questions. By comparing a student's answer to a set of expected answers, the system determines how much information is covered and how to continue the tutorial. Despite the success of LSA in tutoring conversations, the system sometimes has difficulties determining at an early stage whether or not an expectation is covered. A new LSA algorithm significantly improves the precision of AutoTutor's natural language understanding and can be applied to other natural language understanding applications.