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Along with the rapid improvements of informational technology, educational data grows quickly. Such data become massive and raw data. Researchers develop educational standards to regular such data. However, the standards are multiple and the education resources based on different education standards have different structure, which is hard to be shared. Most of them have become Information Islands...
Web contents are going overwhelming today. The numerous online documents, webpages, e-books, etc. are much useful but obtaining them is also time-consuming. Text categorization is one of the solutions to the issue. For all text categorization method, Support Vector Machines (SVM) is one of the most acceptable one. However, to perform more efficiently on webpages, it is necessary to add improvements...
Since term frequency, the most popular discriminator used in term weighting of Natural language processing (NLP), is not the only one which is necessary to be considered when calculating the term weight and make it suitable to indicate term importance, we are motivated to investigate other statistical characteristics of terms and found an important discriminator: term distribution. It is found in...
Term Weighting is a significant step in Document formalization in Natural Language Processing. It greatly interferes the accuracy of natural language processing systems. Term weight consists of three parts: Global Term Weight, Local Term Weight and standardization factor. Many term weight algorithms have been presented to address each part. And currently, the final term weight is the product of multiple...
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