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The problem of automatically extracting the most interesting and relevant keyword phrases in a document has been studied extensively as it is crucial for a number of applications. These applications include contextual advertising, automatic text summarization, and user-centric entity detection systems. All these
Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, but properties of features have not been well investigated. In most cases, a group of
knowledge of the data. The system can constantly incorporate incoming documents from a continuous source into existing visualization context, which is “physically” achieved by minimizing a potential energy defined from similarities between documents. Unlike most existing methods, our system uses dynamic keyword
The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to apply this framework to handwritten word-spotting. Given a word image and a keyword generative model, the idea is to generate a vector which
traditional keyword based search, and provides recommendation that fits the user's personal preferences better. We demonstrate our method by applying it to product review recommendation based on user preferred composition style.
proposed a formalized model of the text semantic similarity and similarity algorithm based on the case grammar. The semantic meanings of a sentence stem decide the similarity of a sentence. To the similarity sentence, a vector is used for the decorating case to get similarity algorithm. In this way, it avoided the keyword
At present,the internet pornographic text is in varied forms and changeful, although it is prohibited ever. It severely harms people's mental and physical health development and social stability. There are IP-based,keyword-based and intelligent content analysis filtering system against it today. But they are difficult
Abstract-By analyzing the process of classification and MapReduce computing paradigms, it is found that the parallel and distributed computing model in MapReduce is appropriate for constructing classifier model. This paper presents a MapReduce algorithm for parallel and distributed classification, aiming to reduce the computational time in training process on large scale documents. Our experiment...
term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords
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