1. A method of performing text classification based on language-independent text features, the method comprising: performing, by a processor, a first syntactic and semantic analysis of a training natural language text to produce a first plurality of language-independent semantic structures representing a plurality of sentences of the training natural language text; producing, based on the first plurality of language-independent semantic structures, a text classifier model; performing a second syntactic and semantic analysis of an input natural language text to produce a second plurality of language-independent semantic structures representing a plurality of sentences of the input natural language text; extracting, using the second plurality of language-independent semantic structures, a set of features, wherein at least one feature references a semantic class of a language-independent semantic hierarchy comprising a plurality of semantic classes, in which the semantic class exhibits one or more properties inherited from its parent semantic class; applying the text classifier model to the set of features to produce a classification spectrum comprising a plurality of weight values, wherein each weight value reflects a degree of association of the input natural language text with a particular category of natural language texts; and associating the input natural language text with one or more categories using the classification spectrum.
1. A method of performing text classification based on language-independent text features, the method comprising: performing, by a processor, a first syntactic and semantic analysis of a training natural language text to produce a first plurality of language-independent semantic structures representing a plurality of sentences of the training natural language text; producing, based on the first plurality of language-independent semantic structures, a text classifier model; performing a second syntactic and semantic analysis of an input natural language text to produce a second plurality of language-independent semantic structures representing a plurality of sentences of the input natural language text; extracting, using the second plurality of language-independent semantic structures, a set of features, wherein at least one feature references a semantic class of a language-independent semantic hierarchy comprising a plurality of semantic classes, in which the semantic class exhibits one or more properties inherited from its parent semantic class; applying the text classifier model to the set of features to produce a classification spectrum comprising a plurality of weight values, wherein each weight value reflects a degree of association of the input natural language text with a particular category of natural language texts; and associating the input natural language text with one or more categories using the classification spectrum. 2. The method of claim 1 , wherein the second syntactic and semantic analysis further includes determining a grammatical feature of the input natural language text.