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One of the challenging problem that Web service technology is now facing is effective service discovery. To solve the deficiencies of Web service description, matching and choosing under WSDL language, this paper presents a web service discovery method based on keyword clustering and concept expansion, mainly from the
Language model adaptation using text data downloaded from the WWW is an efficient way to train a topic-specific LM. We are developing an unsupervised LM adaptation method using data in the Web. The one key point of unsupervised Web-based LM adaptation is how to select keywords to compose the search query. In this
This paper proposes a structure that automatically analyzes the parameters of Chinese test items. This structure utilizes latent semantic analysis (LSA) to analyze the relationships of keywords among all test items in an item bank. It also uses the similarity measure to calculate the similarity degree of keywords. We
This paper presents the comparison of the text document space dimension reduction and the text document clustering and also the keyword space dimension reduction and keyword clustering by the latent semantic analysis and by the Hebbian neural network with Oja learning rule. Results of this neural network are compared
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