8. The method of claim 6 , further comprising: validating the set of values of the feature extraction parameters to produce a quasi-optimal set of values relative to a third plurality of natural language texts.
6. A method, comprising: identifying a plurality of hyper-parameters of a text classifier model, wherein the plurality of hyper-parameters include a number of nearest neighbors to be analyzed by the text classifier model; 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 hyper-parameters of the text classifier model, 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 hyper-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 hyper-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.