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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
. First, the related textual information associated with Web images is identified as the candidate annotations for Web images. Second, the word co-occurrence is utilized to eliminate irrelevant keywords for improving the annotation accuracy. Then, the keyword-based association analysis is exploited to further discover
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.
scenes by checking the discovered cross-media correlation. To make these two modalities comparable, photos related to the visited scenic spots are retrieved from image search engines, by the keywords extracted from text-based schedules. Sequences of key frames and retrieved photos are represented as visual word histograms
of cultural information. Therefore, text categorization research has become more important. The paper improved the precision of the traditional text categorization by the process that we mended the weight of words and mined potential keywords, then found their relationship. In the end of the paper, an experiment was
their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as
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
Image annotation is a challenging task that allows to correlate text keywords with an image. In this paper we address the problem of image annotation using Kernel Multiple Linear Regression model. Multiple Linear Regression (MLR) model reconstructs image caption from an image by performing a linear transformation of
In order to overcome the defects of traditional-method filtering which based on keywords, the OWL text filtering is presented in the semantic Web environment, which makes information filtering, has been raised to the level of semantics. Through distinguishing the information between the title and text, and then
With rapid development of Internet information, It is quite an important project for data mining that how to classify these large amounts of texts. In this paper, we propose an improved text classify cluster algorithm, while calculating similarity, we synthetically consider the relationship between keywords and
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