Invention Machine Corporation

Invention Machines Corporation

Dzianis Pastanohau

James Todhunter

Igor V Sovpel

Alexander Vorontsov

Alexei Vertel

A Semantic Processor for the recognition of Cause-Effect relations in natural language documents which includes a Text Preformatter, a Linguistic Analyzer and a Cause-Effect Knowledge Base Generator. The Semantic Processor provides automatic recognition of cause-effect relation both inside single fact and between the facts in arbitrary text documents, where the facts are also automatically extracted from the text in the form of seven-field semantic units. The recognition of Cause-Effect relations is carried out on the basis of linguistic (including semantic) text analysis and a number of recognizing linguistic models built in the form of patterns.

Semantic processor for recognition of cause-effect relations in natural language documents

A semantic processor and method for automatically recognizing Whole-Part relations in at least one natural language electronic or digital document recognizes one or more expanded Subject-Action-Object (eSAO) sets in text, wherein each eSAO set has one or more eSAO components; matches the one or more eSAO sets against Whole-Part relationship patterns, and generates one or more eSAO Whole-Part relations based on the matching, wherein the eSAO Whole-Part relation comprises a Whole eSAO and an Part eSAO.

Semantic processor for recognition of whole-part relations in natural language documents

A question-answering system for searching exact answers in text documents provided in the electronic or digital form to questions formulated by user in the natural language is based on automatic semantic labeling of text documents and user questions. The system performs semantic labeling with the help of markers in terms of basic knowledge types, their components and attributes, in terms of question types from the predefined classifier for target words, and in terms of components of possible answers. A matching procedure makes use of mentioned types of semantic labels to determine exact answers to questions and present them to the user in the form of fragments of sentences or a newly synthesized phrase in the natural language. Users can independently add new types of questions to the system classifier and develop required linguistic patterns for the system linguistic knowledge base.

Question-answering system and method based on semantic labeling of text documents and user questions

Systems and methods for automatic semantic labeling of natural language documents provided in electronic or digital form include a semantic processor that performs a basic linguistic analysis of text, including recognizing in the text semantic relationships of the type objects and/or classes of objects, facts and cause-effect relationships; matching linguistically analyzed text against target semantic relationship patterns, created by generalization of particular cases of target semantic relationships; and generating semantic relationship labels based on linguistically analyzed text and a result of the matching.

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Minsk, Belarus

Dzianis Pastanohau