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English Article Style Recognition and Matching by Using Web Semantics
Abstract
With the explosion of internet information, people feel helpless and difficult to choose in the face of massive information. However, the traditional method to organize a huge set of original documents is not only time-consuming and laborious, but also not ideal. The automatic text classification can liberate users from the tedious document processing work, recognize and distinguish different document contents more conveniently, make a large number of complicated documents institutionalized and systematized, and greatly improve the utilization rate of information. This paper adopts termed-based model to extract the features in web semantics to represent document. The extracted web semantics features are used to learn a reduced support vector machine. The experimental results show that the proposed method can correctly identify most of the writing styles.
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