Abbyy Production LLC

Abbyy Development Inc.

Abbyy Infopoisk LLC

Stepan Evgenyevich Matskevich

Anatoly Starostin

Dmitry Andreevich Sukhodolov

Ilya Aleksandrovich Bulgakov

Konstantin Anisimovich

Tatiana Danielyan

Ruslan Garashchuk

Andrey Alexandrovich Belov

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 automatic generation of templates for information extraction rules to extract information objects from natural language texts. An example method of automatic generation of templates for information extraction comprises receiving, by a computer device, a first text fragment, comprising a first identifier of a first text token, wherein the first token comprises one or more words in a natural language and wherein the first token references a first information object from a first category of information objects; displaying, using a template-generating graphical user interface, a plurality of linguistic characteristics of the first token; receiving, via the template-generating graphical user interface, a first input identifying template attributes from the plurality of the linguistic characteristics of the first information object; generating a first template based at least in part on the identified template attributes; creating a first production rule for the first template; applying the first production rule to portions of a first natural language text matching the first template; displaying, using the template-generating graphical user interface, a second information object identified in the first natural language text using the first production rule.

System and method of automatic template generation

Systems and methods for extracting facts from natural language texts. An example method of information extraction comprises extracting, from a natural language text, a first plurality of information objects; extracting, from the natural language text, a second plurality of information objects; identifying a set of conflicting information objects, such that a first information object of the set of conflicting information objects belongs to the first plurality of information objects and a second information object of the set of conflicting information objects belongs to the second plurality of information objects; and producing a final list of information objects extracted from the natural language text, by applying, to the set of conflicting information objects, a conflict arbitration function which performs at least one of: modifying the first information object, deleting the first information object, or merging two or more information objects of the set of conflicting information objects.

Information extraction from natural language texts

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

Systems and methods for determining confidence levels associated with attribute values of informational objects. An example method comprises: receiving a natural language text; performing syntactico-semantic analysis of the natural language text to produce a plurality of semantic structures; interpreting the plurality of semantic structures using a set of production rules to produce a plurality of data items, each data item associating an attribute value with an informational object representing an entity referenced by the natural language text; and determining, for at least one data item of the plurality of data items, a confidence level associated with the attribute value, by evaluating a confidence function associated with the set of production rules.

Determining confidence levels associated with attribute values of informational objects

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.

Systems and methods for task distribution are provided. A total number of available computing system's processing units is defined, where the total number of available processing units includes a set of regular processing units available for executing tasks and a set of processing units that constitute the reserve pool. Tasks are assigned to processing units. The number of processing units assigned to the next task in the queue is no more than the total number of processing units available at the time, multiplied by the availability ratio. Iterative assignment of processing units to tasks according to the method described is performed as long as there are idle processing units available for task execution, when no more processing units are available, the processing units from the reserve pool are assigned. As a result, the method allows processing units to be available for allocation to a new incoming task at any time.

Growing community of inventors

Korolev, Russia

Stepan Evgenyevich Matskevich