10. The system of claim 9 , wherein the processor is further to: determine a first rating of the trained classification model using a first subset of the set of token attributes; identify a second subset of the set of token attributes, wherein the second subset comprises the first subset of token attributes and an additional token attribute; determine a second rating of the trained classification model using the second subset of token attributes; and select one of the first subset of token attributes or the second subset of token attributes based on the first rating and the second rating.
9. A system comprising: a memory to store a natural language text; and a processor, operatively coupled to the memory, to: identify a set of training texts; extract a respective set of features for each of the training texts; train a classification model using the training texts and the extracted features; extract a token from the natural language text; identify a set of token attributes associated with the token based on a semantic-syntactic analysis of the natural language text, wherein the set of token attributes comprises at least one of a lexical attribute, a syntactic attribute, or a semantic attribute, and wherein to perform the semantic-syntactic analysis of the natural language text, the processor is to: generate a lexical-morphological structure of a sentence of the natural language text; identify a syntactic tree using the lexical-morphological structure; generate a language-independent semantic structure based on the syntactic tree; and identify the set of token attributes using the language-independent semantic structure; determine a category for the token based on the trained classification model and the set of token attributes; and generate a tagged representation of the natural language text, the tagged representation referencing the category for the token.