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The majority of the scientific papers include keywords besides the obligatory title and abstract. The use of keywords is not just for the description of content, but it is a viral part of the scientific paper which is later used for information retrieval function. The aim of this paper is to present comparison of
semantic net which can be applied to build personalized search engine and tested with single query keyword and multi ones by three different calculating policies. The test results show that it can affect the sort of pages. The personalized search based on vocabulary semantic net improves the quality of search results greatly.
Automatic image annotation is a promising solution to enable more effective image retrieval by keywords. Different statistical models and machine learning methods have been introduced for image auto-annotation. In this paper, we propose a collaborative approach, in which multiple different statistical models are
Image annotation is usually formed as a multiclass classification problem. Traditional methods learn the co-occurrence of keywords and images while they ignore the correlation between keywords, which turned out to be one of the reasons causing poor experiment results. In this paper, we propose an automatic image
Automatic annotating images by equipment is of great interest as it meets one's common need for retrieving image content. Usually image content description with keywords is regarded as a visual-word correlation process. However, in view of the viewer's psychology, image to words is a kind of cognition process, which
This paper aims to analyze affective expressions in articles of popular science by text mining with the keywords “Cancer” and “Immunity”. This study selects 145 articles from the website of a magazine and segmented them into 410,919 terms. And the study uses an automatic system to classify the terms into vocabulary
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