Abstract: A document marking projection system receives a target document comprising text content, determines a set of similar documents that are similar to the target document, and selects a first similar document from the set of similar documents. The document marking projection system identifies a first marking within the first similar document, determines a projected marking for the target document in view of one or more differences between text content in the first similar document and the text content in the target document, and stores the projected marking for the target document.

Abstract: Systems and methods for marking a natural language text to identify segments of different types. The method includes using a classification model to classify candidate segments as belonging to one of the types where the classifiers of the training model are trained on a marked natural language text.

Abstract: A document marking projection system receives a target document comprising text content, determines a set of similar documents using an index of stored documents, where the set of similar documents are similar to the target document, and selects a first similar document from the set of similar documents that is most similar to the target document. The document marking projection system determines one or more portions of text content in the first similar document that are different from respective one or more portions of text content in the target document, determines a first location of a first marking within the first similar document, determines a projected marking for the target document in view of one or more differences between the first portion of the text content in the first similar document and a respective portion of the text content in the target document, and stores the projected marking for the target document.

Abstract: A document marking projection system receives a target document comprising text content, determines a set of similar documents using an index of stored documents, where the set of similar documents are similar to the target document, and selects a first similar document from the set of similar documents that is most similar to the target document. The document marking projection system determines one or more portions of text content in the first similar document that are different from respective one or more portions of text content in the target document, determines a first location of a first marking within the first similar document, determines a projected marking for the target document in view of one or more differences between the first portion of the text content in the first similar document and a respective portion of the text content in the target document, and stores the projected marking for the target document.

Abstract: Systems and methods for evaluating text classifier parameters based on semantic features. An example method comprises: performing a semantico-syntactic analysis of a natural language text of a corpus of natural language texts to produce a semantic structure representing a set of semantic classes; identifying a natural language text feature to be extracted using a set of values of a plurality of feature extraction parameters; partitioning the 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 category of the training data set, the set of values of the feature extraction parameters; validating the set of values of the feature extraction parameters using the validation data set.

Abstract: An example method for natural language text classification based on semantic features comprises: performing semantico-syntactic analysis of a natural language text to produce a semantic structure representing a set of semantic classes; associating a first semantic class of the set of semantic classes with a first value reflecting a specified semantic class attribute; identifying a second semantic class associated with the first semantic class by a pre-defined semantic relationship; associating the second semantic class with a second value reflecting the specified semantic class attribute, wherein the second value is determined by applying a pre-defined transformation to the first value; evaluating a feature of the natural language text based on the first value and the second value; and determining, by a classifier model using the evaluated feature of the natural language text, a degree of association of the natural language text with a category of a pre-defined set of categories.

Abstract: An example method for natural language text classification based on semantic features comprises: performing semantico-syntactic analysis of a natural language text to produce a semantic structure representing a set of semantic classes; associating a first semantic class of the set of semantic classes with a first value reflecting a specified semantic class attribute; identifying a second semantic class associated with the first semantic class by a pre-defined semantic relationship; associating the second semantic class with a second value reflecting the specified semantic class attribute, wherein the second value is determined by applying a pre-defined transformation to the first value; evaluating a feature of the natural language text based on the first value and the second value; and determining, by a classifier model using the evaluated feature of the natural language text, a degree of association of the natural language text with a particular category of a pre-defined set of categories.

Abstract: Systems and methods for evaluating text classifier parameters based on semantic features. An example method comprises: performing a semantico-syntactic analysis of a natural language text of a corpus of natural language texts to produce a semantic structure representing a set of semantic classes; identifying a natural language text feature to be extracted using a set of values of a plurality of feature extraction parameters; partitioning the 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 category of the training data set, the set of values of the feature extraction parameters; validating the set of values of the feature extraction parameters using the validation data set.