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Online advertising has now turned to be one of the major revenue sources for today's Internet companies. Among the different channels of advertising, contextual advertising takes the great part. There are already lots of studies done for the keyword extraction problem in contextual advertising for English, however
(MWE) and they do not scale very well. This paper proposes a clustering and classification algorithm for semantic similarity using sample web pages. Further improvement is to analyze the short text for classification and labeling the short text according to the keyword and producing the result for the end user. This type
(MWE) and they do not scale very well. This paper proposes a clustering and classification algorithm for semantic similarity using sample web pages. Further improvement is to analyze the short text for classification and labeling the short text according to the keyword and producing the result for the end user. This type
to take advantage of prior knowledge about the relation of genes and a disease. In the proposed approach, keyword scanning of human proteins at the Swissprot database is performed to select genes related to the disease of interest followed by analysis of differential gene expressions. In results obtained on lung cancer
likelihood in the entire training documents where the training and test data are split randomly into k-subsets like 2/3 for training and 1/3 for test data. In addition, it also utilizes two level hierarchy structures for training documents like features from title, keywords and content with the predefined knowledge available
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