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A common strategy to assign keywords to documents is to select the most appropriate words from the document text. One of the most important criteria for a word to be selected as keyword is its relevance for the text. The tf.idf score of a term is a widely used relevance measure. While easy to compute and giving quite
number of keywords for the searches of the events, especially in relation with analytics and searches on SNs for the reflectance of those events. A special attention is given to synonyms.
This paper presents contextual kernel and spectral methods for learning the semantics of images that allow us to automatically annotate an image with keywords. First, to exploit the context of visual words within images for automatic image annotation, we define a novel spatial string kernel to quantify the similarity
Expansion of query keywords based on semantic relationship is an effective approach to improve the performance of text retrieval. In this paper, a novel approach for text retrieval is presented. The principle of the approach is to construct a integrated semantic tree, and select candidate keywords from the tree. On
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
trigger keywords and contextual cues. The system was tested on multiple large collections of Dutch tweets. Our experimental results show that our system can successfully analyze messages and recognize threatening content.
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