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In traditional Vector Space Model (VSM) the TF*IDF method is widely used to adjust the weight of terms in text mining. However TF*EDF can not represent the semantic information of text by neglecting the semantic relevance between terms. In this paper, an improved ontology-based VSM is presented, in which the ontology-based term similarity is used to readjust the weight of semantically related terms...
In this article we present a 2D cellular automaton (Class_AC) to solve a problem of text mining in the case of unsupervised classification (clustering). Before to experiment the cellular automaton, we vectorized our data indexing textual documents from the database REUTERS 21,578 by the approach of N-grams. The cellular automaton that we propose in this paper is a grid cell structure with a flat neighborhood...
In this age of awareness, people have access to information like never before. Hundreds of newspapers and millions of bloggers present news and their interpretations in an openly accessible manner. With globalization, distant events can have impact on people thousands of miles away. While expert humans can recognize a potentially important piece of news, this is still a difficult problem for an automatic...
Text representation is the basis of text processing. Most current text representation models ignore the words' inter-relations, which result in the loss of textpsilas structure information. This paper proposed a novel text representation model, which uses lexical network to represent the text and retains the text's structure. According to the different levels of words' inter-relations, co-occurrence...
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