A Revised Algorithm for Latent Semantic Analysis

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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.

Xiangen Hu, Zhiqiang Cai, Max M. Louwerse, Andrew

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AutoTutor's Natural Language | IJCAI 2003 | IJCAI 2007 | Latent Semantic Analysis | Natural Language Understanding |

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Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2003
Where	IJCAI
Authors	Xiangen Hu, Zhiqiang Cai, Max M. Louwerse, Andrew Olney, Phanni Penumatsa, Arthur C. Graesser

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A Revised Algorithm for Latent Semantic Analysis

AutoTutor's Natural Language | IJCAI 2003 | IJCAI 2007 | Latent Semantic Analysis | Natural Language Understanding |

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