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The Reuters-21578 Text Categorization Test Collection.
Text representation is a necessary procedure for text categorization tasks.
Therefore, many different learning approaches have been designed in the text categorization field.
Most of the research on text categorization didnot consider the characteristics of the emergency domain.
However the major problem of text categorization is the high dimensionality of the feature space.
Text categorization is one of the most common themes in data mining and machine learning fields.
Text categorization systems are designed to classify documents into a fixed number of predefined categories.
This paper proposes a concise semantic analysis (CSA) technique for text categorization tasks.
A new measure function of Gini index is constructed and made to fit text categorization.
Automatic text categorization becomes more and more important for dealing with massive data.
However, existing SA techniques are not designed for text categorization and often incur huge computing cost.
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