1. A method, comprising: identifying a plurality of feature extraction parameters of a text classifier model, wherein the plurality of feature extraction parameters comprises a first attribute of a first semantic class and a second attribute of a second semantic class, wherein a value of the second attribute is produced by applying a pre-defined transformation to a value of the first attribute; partitioning a corpus of natural language texts into a training data set comprising a first plurality of natural language texts and a validation data set comprising a second plurality of natural language texts; determining, in view of the training data set, a set of values of the feature extraction parameters, which maximizes a number of natural language texts of the validation data set that are classified correctly by the text classifier model using the set of values of the feature extraction parameters; performing, by a processing device, a semantico-syntactic analysis of an input natural language text to produce a semantic structure representing a set of semantic classes; producing a plurality of values by applying, to the semantic structure representing the input natural language text, the text classifier model using the set of values of the feature extraction parameters, wherein each value of the plurality of values reflects a degree of association of the input natural language text with a particular category of natural language texts; associating the input natural language text with a category corresponding to an optimal value among the plurality of values; and utilizing the category to perform a natural language processing task.
1. A method, comprising: identifying a plurality of feature extraction parameters of a text classifier model, wherein the plurality of feature extraction parameters comprises a first attribute of a first semantic class and a second attribute of a second semantic class, wherein a value of the second attribute is produced by applying a pre-defined transformation to a value of the first attribute; partitioning a corpus of natural language texts into a training data set comprising a first plurality of natural language texts and a validation data set comprising a second plurality of natural language texts; determining, in view of the training data set, a set of values of the feature extraction parameters, which maximizes a number of natural language texts of the validation data set that are classified correctly by the text classifier model using the set of values of the feature extraction parameters; performing, by a processing device, a semantico-syntactic analysis of an input natural language text to produce a semantic structure representing a set of semantic classes; producing a plurality of values by applying, to the semantic structure representing the input natural language text, the text classifier model using the set of values of the feature extraction parameters, wherein each value of the plurality of values reflects a degree of association of the input natural language text with a particular category of natural language texts; associating the input natural language text with a category corresponding to an optimal value among the plurality of values; and utilizing the category to perform a natural language processing task. 2. The method of claim 1 , wherein partitioning the corpus of natural language texts comprises cross-validating the first plurality of natural language texts and the second plurality of natural language texts.