Scoring Dialogue in Chinese Intelligent Tutoring System Based on Weighted Latent Semantic Analysis
Date Issued
2011
Date
2011
Author(s)
Chen, Jia-Yi
Abstract
In this study we followed the generic framework of intelligent tutoring systems (ITS) and constructed an ITS platform to investigate computer-assisted instruction with natural language dialogue. Unlike the literal-matching, Latent Sematic Analys (LSA) is utilized primarily to model higher level cognition as an approach of concept-matching. LSA as a statistical model of human language knowledge representation has been highly successful in many different areas. However, the neglect of word order and syntactic information becomes the primary limitation of LSA. The notion of adding syntactic information to improve the limitation of LSA is proposed in this work. The idea is that the weight of each element of vectors was adjusted according to its syntactic structure in a sentence. The Sinica Treebank is adopted as foundation of weight determination. Three kinds of weighting function were proposed and positive results had been tested. The weighting function 2 powered by n provided highest relative precision and accuracy among those weighting functions. In addition to LSA, the results revealed that weighting function is also effective for traditional vector space models.
Subjects
Scoring dialogue
latent semantic analysis
weighting
syntactic information
Type
thesis
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