Invention Machine Corporation

Invention Machines Corporation

Igor V Sovpel

James Todhunter

Dzianis Pastanohau

Valery M Tsourikov

Leonid S Batchilo

Alexander Vorontsov

Alexei Vertel

Alexander Korzun

Heorhi Zhyhalko

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.

System and method for automatic semantic labeling of natural language texts

Answering System enables a user to input a query and responds with answers. Server stores an indexed database of a plurality of S-A-O's (subject-action-object). User generates the query that includes query elements (A-O), (S-A), or (S-X-O), or element (S). The server identifies the database element S, O, or A, or elements (A-O) associated with a respective knowledge base S-A-O that includes the query element(s) in response to the server receiving the query and generates a natural language answer S-A-O that includes the identified and the query elements(s) and transmits the answer to the user apparatus that provides an audio or visual response. The server can conduct WWW searches for documents with S-A-O elements that match query element(s) and adds them to the database, thus treating the WWW as an available knowledge base.

Semantic answering system and method

A system and method for cross-language knowledge searching. The system has a Semantic Analyzer, a natural language user request/document search pattern/semantic index Generator, a user request search pattern Translator and a Knowledge Base Searcher. The system also provides automatic semantic analysis and semantic indexing of natural language user requests/documents on knowledge recognition and cross-language relevant to user request knowledge extraction/searching. System functionality is ensured by Linguistic Knowledge Base as well as by a number of unique bilingual dictionaries of concepts/objects and actions.

System and method for cross-language knowledge searching

A system and method for summarizing the contents of a natural language document provided in electronic or digital form includes preformatting the document, performing linguistic analysis, weighting each sentence in the document as a function of quantitative importance, and generating one or more document summaries, from a plurality of selectable document summary types, as a function of the sentence weights.

Computer based summarization of natural language documents

A computer based software system and method for semantically processing a user entered natural language request to identify and store linguistic subject-action-object (SAO) structures, using such structures as key words/phrases to search local and web-based databases for downloading candidate natural language documents, semantically processing candidate document texts into candidate document SAO structures, and selecting and storing only relevant documents whose SAO structures include a match with a stored request SAO structure. Further features include analyzing relationships among relevant document SAO structures and creating new SAO structures based on such relationships that may yield new knowledge concepts and ideas for display to the user and generating and displaying natural language summaries based on the relevant document SAO structures.

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Minsk, Belarus

Igor V Sovpel