Abbyy Production LLC

Abbyy Infopoisk LLC

Abbyy Development Inc.

Anatoly Starostin

Stepan Evgenyevich Matskevich

Dmitry Andreevich Sukhodolov

Egor Yakovlev

Konstantin Zuev

Konstantin Anisimovich

Vladimir Pavlovich Selegey

Tatiana Danielyan

Ivan Smurov

Maria Stepanova

Dmitrii Kuklin

Ilia Bulgakov

Techniques for machine-based identification of objects extracted from text documents in natural language are disclosed. An example method may comprise: identifying matching pairs of one or more information objects corresponding to a real world object, one information object from the document and at least one information object from the document storage for a combination of global identification patterns that exist in the document and in the document storage; ascertaining consistency of the matching pairs and determining which of the one or more information objects in the document are suitable for merging into the document storage; and adding the one or more information objects from the document to the document storage to associate information objects corresponding to the real world object.

System and method for global identification in a collection of documents

Systems and methods for information extraction from natural language texts using a combination of classifier models. An example method may comprise: producing, by performing syntactico-semantic analysis of a natural language text, a plurality of syntactico-semantic structures representing the natural language text; identifying, using a first classifier model to process a first plurality of classification attributes derived from the syntactico-semantic structures, a plurality of core constituents, such that each core constituent of the plurality of core constituents is associated with a span of a plurality of spans, wherein each span represents an attribute of an information object of a specified ontology class; identifying, using a second classifier model to process a second plurality of classification attributes derived from the syntactico-semantic structures, child constituents of each of the plurality of core constituents; and determining, using a third classifier model to process a third plurality of classification attributes derived from the syntactico-semantic structures, whether a first span of the plurality of spans and a second span of the plurality of spans represent information object attributes that are associated with a same information object.

Information object extraction using combination of classifiers

Disclosed are system, method and computer program product for synthesis of natural-language text; receiving information objects; selecting among the received information objects information objects and an associated synthesis templates in a template library, each synthesis template including a template semantic-syntactic tree; generating for each selected information object a synthesis semantic-syntactic tree based on the template semantic-syntactic tree; and generating natural language text based on each generated synthesis semantic-syntactic tree.

Method and system of text synthesis based on extracted information in the form of an RDF graph making use of templates

Techniques for machine-based identification of objects extracted from text documents in natural language are disclosed. Text documents with extracted objects are presented in a form of Resource Description Framework (RDF) graphs with the nodes correspondent to the objects and arcs correspondent to the relations between objects. Identification of objects is implemented using specific combinations of patterns which define features of the objects.

A method and system of machine-based extraction of information from a text document employ the steps of performing semantic/syntactic analysis for sentences of the document to build semantic-syntactic structures of the sentences; applying production rules to the semantic-syntactic structures to generate a set of logical conclusions about objects of information comprised in the document, wherein the production rules are based on linguistic characteristics and lexical-morphological properties of the semantic-syntactic structures and ontologies of subject matters of the sentences; and using the set of logical conclusions about objects of information comprised in the document to build an ontology-based RDF graph.

Method and system for machine-based extraction and interpretation of textual information

A method, system, and computer program product for generating and using a user semantic dictionary for natural language involving receiving a user dictionary element definition associated with a user ontological object; generating the user semantic dictionary based on the user dictionary element definition, analyzing by a hardware processor the user-provided text to identify a portion of the user-provided text matching the semantic-syntactic data; generating a node in a semantic-syntactic tree, and further natural language processing of the user-provided text using the generated semantic-syntactic tree. The semantic-syntactic tree includes connected nodes, including references to the user ontological object or a reference to the first semantic dictionary structure. The generated user semantic dictionary includes a semantic dictionary structure with semantic-syntactic data and an identifier of the user ontological object.

System and method for generating and using user semantic dictionaries for natural language processing of user-provided text

A system for natural language processing is provided. A first natural language processing program may be constructed using language-independent semantic descriptions, and language-dependent morphological descriptions, lexical descriptions, and syntactic descriptions of one or more target languages. The natural language processing program may include any of machine translation, fact extraction, semantic indexing, semantic search, sentiment analysis, document classification, summarization, big data analysis, or another program. Additional sets of natural language processing programs may be constructed.

Exhaustive automatic processing of textual information

A method, system, and computer program product for generating and using a user ontological model for natural language processing of user-provided text, including receiving definitions of user ontological objects and generating user ontological models. A semantic-syntactic tree generated from user-provided text is analyzed. Information objects based on the user ontological objects are generated by the analysis.

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Moscow, Russia

Anatoly Starostin