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expansion of the keyword lexicon used to discover IED related web pages, which identified new relevant terms for inclusion. Additionally, we present an improved web page feature representation designed to better capture the structural and stylistic cues revealing of genres of communication, and a series of experiments
Social tagging allows users to assign keywords (tags) to resources facilitating their future access by the tag creator, and possibly by other users. In terms of its support for resource discovery, social tagging has both proponents and critics. The goal of this paper investigates if tags are an effective means for
Domain — specific search focuses on one area of knowledge. Applying broad based ranking algorithms to vertical search domains is not desirable. The broad based ranking model builds upon the data from multiple domains existing on the web. Vertical search engines attempt to use a focused crawler that index only relevant web pages to a predefined topic. With Ranking Adaptation Model, one can adapt an...
of HTML page, and the proposed algorithms is performed. Complete evaluation is performed which indicates the effectiveness of using our technique. The experimental results show improved precision and recall with the proposed algorithms with respect to keyword-based search. The algorithms are implemented in JAVA and its
Traditional automatic classifiers often conduct misclassifications. Folksonomy, a new manual classification scheme based on tagging efforts of users with freely chosen keywords can effective resolve this problem. Even though the scalability of folksonomy is much higher than the other manual classification schemes, the
Traditional information retrieval (IR) method use keywords matching to filter the documents, but usually retrieves unrelated Web pages. In order to effectively classify Web pages, we present a Web page categorization algorithm, named WebPSC (Web page similarity categorization). This algorithm uses latent semantic
obtain latent semantic structure of original term-document matrix solving the polysemous and synonymous keywords problem. LS-SVM is an effective method for learning the classification knowledge from massive data, especially on condition of high cost in getting labeled classical examples. We adopt a novel method of Web page
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