Fact Repository Error Correction for Semantic Text
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current error correction systems in computerized text and voice processing, as well as data repositories, are inadequate in addressing context and semantic errors, failing to correct complex mistakes such as misinterpreted names, locations, and relationships beyond simple mismatches.
Innovation Solution
A system and method utilizing fact repositories to search for and compare relevant facts associated with input digital text, extracting and analyzing differences to transform the text into corrected form, leveraging large-scale factual information from resources like the Web and databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If language models are used for error correction, then spelling and grammatical errors can be corrected, but context and semantic errors cannot be corrected
Solution Approach 1:
The system extends error correction capability from handling only spelling and grammatical errors to also handling context and semantic errors by integrating multiple data sources including language models, fact repositories, and knowledge graphs, making the correction system universally applicable to diverse error types
2Ease of manufacture
If simple mismatch correction is used, then zip code and town name errors can be corrected, but complex errors in content and metadata cannot be corrected
Solution Approach 1:
The system introduces fact repositories and knowledge graphs as intermediary components between the simple mismatch correction and complex error correction, enabling the system to handle complex content and metadata errors while maintaining the simplicity of the core correction mechanism
3Productivity
If automated error correction systems are used, then processing speed is improved, but ability to handle complex semantic errors deteriorates
Solution Approach 1:
The system performs preliminary action by pre-building and storing factual information in fact repositories and knowledge graphs before error correction is needed, enabling fast automated retrieval and comparison during the correction process without sacrificing semantic understanding capability
Data Source
AI summary
The disclosed system and method apply stores of factual information to correct errors in digital text, for example, generated from OCR, speech and/or handwriting recognition devices, and other automatic recognition devices. A text produced by OCR, speech recognition, handwriting recognition, and others may be processed to extract discussed facts. Databases of facts are searched based on information in the text. After comparing facts asserted in the text with the factual data from the databases, suggested corrections of the text are produced.