17. The method as recited in claim 16 , wherein determining the value for each of the multiple words includes determining a difference value between a highest word frequency vector and a lowest word frequency vector among the corresponding word frequency vectors for each of the multiple words at the plurality of classes.
16. A computer-implemented method for text classification, the method comprising: receiving a text for classification into a plurality of classes, at least one of the plurality of classes including one or more direct child classes that are non-leaf child classes in a tree class structure; segmenting content of the received text into multiple words, each of the multiple word having corresponding word frequency vectors; determining a value for each of the multiple words at individual classes of the plurality of classes using the corresponding word frequency vectors of each of the multiple words; determining a sum of the corresponding word frequency vectors for each of the multiple words that has the value higher than a pre-defined filtering threshold; and classifying the text into a class of the plurality of classes that is associated with a sum of the corresponding word frequency vectors that has a highest value.