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: Systems and methods for automatic generation of templates for information extraction rules to extract information objects from natural language texts.

Abstract: Systems and methods for automatic generation of templates for information extraction rules to extract information objects from natural language texts.

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: 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: 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.

Abstract: Disclosed are system and method for storing, searching and updating extracted data for natural language processing of text.

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: 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.