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In this paper, an algorithm for extracting keywords without corpus is described. We use the co-occurrence information of the words and the biases of distribution to extract the more important words based on the most frequently appearing words so called reference words. Firstly, the most frequently terms are chosen
provide simple message analysis features such as browsing and simple keyword-based searching of the recorded messages. In this paper, we propose a system, called IMAnalysis, that supports intelligent chat message analysis using text mining techniques. The IMAnalysis system provides functions on chat message retrieval, social
Semantic soccer video analysis has attracted more and more attention recently. In this paper, we present a football event detection method by using multiple feature extraction and fusion. Instead of using low-level features, the proposed method is built upon visual, auditory features, text and audio keywords
with its topic-specific keywords. A hierarchical relationship of super-topics and sub-topics is defined by a taxonomy, meanwhile, Wikipedia is used to provide context and background knowledge for topics that defined in the taxonomy to guide the term identification and extraction. The experimental results have shown the
degree of relevancy for the user than is currently available with conventional methods, for example, using matching keywords. We describe here our method and the relation between the scenes and discuss a prototype system.
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