Abbyy Production LLC

Abbyy Development Inc.

Evgeny Indenbom

Konstantin Anisimovich

Sergey Kolotienko

Valery Igorevich Novitskiy

Andrey Valerievich Myakutin

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.

Marking comparison for similar documents

Systems and methods for multi-stage recognition of named entities based on morphological and semantic features of natural language texts. An example method comprises: performing a lexico-morphological analysis of a natural language text comprising a plurality of tokens, each token comprising at least one natural language word; determining, based on the lexico-morphological analysis, one or more lexical meanings and grammatical meanings associated with each token of the plurality of tokens; for each token the plurality of tokens, evaluating one or more classifier functions using the lexical and grammatical meanings associated with the tokens, wherein a value of each classifier function is indicative of a degree of association of the token with a category of named entities; performing a syntactico-semantic analysis of at least part of the natural language text to produce a plurality of semantic structures representing the part of the natural language text; and interpreting the semantic structures using a set of production rules to determine, for one or more tokens comprised by the part of the natural language text, a degree of association of the token with a category of named entities.

Multi-stage recognition of named entities in natural language text based on morphological and semantic features

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.

Natural language text classification based on semantic features

Systems and methods for identifying word collocations in natural language texts. An example method comprises: performing, by a computing device, semantico-syntactic analysis of a natural language text to produce a plurality of semantic structures; generating, in view of relationships defined by the semantic structures, a raw list of word combinations; producing a list of collocations by applying a heuristic filter to the raw list of word combinations; and using the list of collocations to perform a natural language processing operation.

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Moscow, Russia

Evgeny Indenbom