A revised algorithm for latent semantic analysis

Authors

Xiangen Hu, University of Memphis
Zhiqiang Cai, University of MemphisFollow
Max Louwerse, University of MemphisFollow
Andrew Olney, University of Memphis
Phanni Penumatsa, University of Memphis
Art Graesser, University of MemphisFollow

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.

Publication Title

IJCAI International Joint Conference on Artificial Intelligence

Recommended Citation

Hu, X., Cai, Z., Louwerse, M., Olney, A., Penumatsa, P., & Graesser, A. (2003). A revised algorithm for latent semantic analysis. IJCAI International Joint Conference on Artificial Intelligence, 1489-1491. Retrieved from https://digitalcommons.memphis.edu/facpubs/7294