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method based on semi-supervised learning to get focuses of social topics in a large amount of text. We develop a novel keyword extraction method named NATF-PDF, which is based on TFPDF algorithm, combined with supervised learning theory for keyword extraction. We compare its performance with TFIDF in comparison, and the
results in finding a suitable template size that can be used to create tiles for visual keywords. These visual keywords are combined with text keywords to create a multimodal image representation before applying clustering.
With the number of registered Web services growing, Identifying desired Web service is crucial for Web users. Current keyword based service search are inefficient in two main aspects: poor scalability and lack of semantics. Firstly ,the users are overwhelmed by the huge number of irrelevant services returned. Secondly
The proliferation of Web services demands for a discovery mechanism to find advertisements that satisfy the requests more accurately. OWL-S provides a capability-based description and logic inference mechanism for semantically matching. UDDI provides a registry of businesses and Web services, but its keyword search
The user enters any query to find desired information. To discover number of user search goals and representing each goal with some keyword, we first infer user search goals for a query by clustering feedback sessions. For that, we use a concept of pseudo document, which is the revised version of feedback session
machine learning approach. The keywords are used to cluster the documents subset. The clustered result is the taxonomy of the subset. Lastly, the taxonomy is modified to the hierarchical structure for user navigation by manual adjustments. The topic digital library is constructed after combining the full-text retrieval and
geo-tagged information associated to tweets, events related to a particular place can be detected using clustering techniques and semantic interpretation of keywords.
effectively. This paper proposes a novel personal topics detection approach using clustering algorithm. First preprocess the emails and construct the improved email VSM(vector space model) to label the email combining the body and subject in a new method, then adopt the advanced k-means algorithm to cluster the emails and design
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