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set. Objectionable Web text detection is performed based on the similarity between the text and the model. Experiments are done on real world text sets which come from Web forums, and the results show that the proposed method can achieve better performance than that of keyword-based method with semantic feature selection
based ranking algorithm is proposed. Query keywords are regarded as heat sources, and a person name which has strong connection with the query (i.e., frequently co-occur with query keywords and co-occur with other names related to query keywords) will receive most of the heat, thus being ranked high. Experiments on the
extract the keywords in each document. The paper establishes the transformation between the keywords in documents and the binary granules, and adopts the algorithm of association rules based on granular computing to obtain frequent item sets between documents. Bring in the set theory thought, numbers of the same word between
gambling is under strict regulations. However, there are so many websites that it is rather difficult to regulate Internet gambling and rather challenging to identify them. It may introduce many false positives or false negatives, if we simply grep contents of websites with keywords. In this paper, we find that the behavior
The wide diffusion of community tagging sites and related folksonomies has made the knowledge discovery and retrieval still much more urgent topic. If tagging systems allow users to add freely keywords to web resources, clicking on a tag has the side effect of a tag-based query, since enables the users to explore
5th GrC model is the formal model specified into the category of sets. It is a theory of ordered granules, namely, granules are ordered ldquosubsetsrdquo of the universe, We extract a 5th GrC model from a set of Web pages. A granule is a high frequent sequence of keywords, It is a tuple in a relation and naturally
term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords
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