Web Text Mining Using a Hybrid System

Fernando Hideo FukudaLuiz Biondi NetoVincenzo de Roberto JuniorElias Restum AntonioLuiz ChiganerEmmanuel L. P. PassosMarco Aurélio Cavalcanti PachecoJorge Valério

This paper presents the research of artificial intelligence techniques based on KDD (Knowledge Discovery in Databases), KDT (Knowledge Discovery in Texts), Expert System and ANN (Artificial Neural Network) applied for evaluation and selection of textual documents found on WWW (World Wide Web). These techniques are useful because nowadays we have a explosive growth of the Web that provides a great amount of documents of many different subjects and the user needs to select these documents regarding to theirs particular interests. We considered the Web as a large data warehouse and applied the KDD fundaments and Text Mining procedures to develop these techniques. The techniques developed are language syntax independent because don't use NLP parser and provide an automatic text evaluation based on user profile interests acquired by examples using ANN. Finally, we developed a system using these techniques and compared with a similar commercial system available in the Web.

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