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Vertical search engines provide Web users with an alternative way to search for information on the Web by providing customized searching in particular domains. However, two issues need to be addressed when developing these search engines: how to locate relevant documents on the Web and how to filter out irrelevant documents from a set of documents collected from the Web. This paper reports the research in addressing the second issue. In this research a machine learning-based approach that combines Web content analysis and Web structure analysis is proposed.