Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for creating ontologies by analyzing natural language texts. An example method comprises: receiving identifiers of a first plurality of word groups within a natural language text, each word group comprising one or more natural language words; associating an object represented by each word group with a concept of an ontology; identifying, within the natural language text, a second plurality of word groups, wherein each word group of the second plurality of word groups is associated with the concept of the ontology; responsive to receiving a confirmation that a word group of the second plurality of word groups represents an object associated with the concept of the ontology, modifying a parameter of a classification model that produces a value reflecting a degree of association of a given object with the concept of the ontology.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed are system, method and computer program product for creating syntactic-semantic structures of natural language sentences in natural language processing of a natural language text, comprising generating syntactic trees for each sentence including syntactic nodes and tree-like syntactic relations; generating semantic structure corresponding to the at least one syntactic tree; the at least one semantic structure includes semantic nodes corresponding to the plurality of syntactic nodes and tree-like semantic relations corresponding to the p tree-like syntactic relations; if a syntactic tree includes two different syntactic nodes corresponding to a single entity, connecting the semantic nodes corresponding to the syntactic nodes by a non-tree link.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.