Automated Natural Language Grammar Generation via Substring Analysis
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Solution Overview
Problem
Building a natural language grammar vocabulary for speech recognition systems is a time-consuming and costly process, requiring human intervention that is not feasible for real-time applications, and existing automated methods lack the speed and accuracy to match human performance.
Innovation Solution
A method and apparatus that automate the creation of natural language grammar sets by tagging input text strings, expanding them with common substitutions, and determining substring relationships to generate accurate grammar rules within a short timeframe, allowing for rapid development of high-accuracy NL grammar sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually build natural language grammar vocabulary, then accuracy is high, but time consumption is excessive (several weeks)
Solution Approach 1:
The system creates copies of existing tagged text strings and uses automated processing to generate grammar rules from these copies, eliminating the need for manual grammar construction while maintaining accuracy through systematic analysis of the copied data patterns
Solution Approach 2:
The system performs self-service by automatically tagging text strings, identifying substitutions, and generating grammar rules without human intervention. The automated engine processes the tagged data itself to create the grammar vocabulary, replacing the need for human experts to manually build the grammar
2Productivity
If automated methods are used to create natural language grammar, then speed increases, but accuracy decreases compared to human performance
Solution Approach 1:
The system incorporates feedback mechanisms where the automated grammar generation process continuously refines its output by comparing generated rules against the tagged data patterns. The system learns from its own performance and adjusts its processing to improve accuracy while maintaining high speed automated operation
Solution Approach 2:
The patent replaces the mechanical human cognitive process of grammar construction with an automated computational system that uses algorithmic processing, pattern recognition, and systematic analysis to generate grammar rules at high speed while achieving accuracy comparable to or exceeding manual methods
3Adaptability or versatility
If comprehensive substitution expansion is performed on input text strings, then grammar coverage improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-tagging text strings and pre-identifying substitution patterns before the actual grammar generation process. This preliminary processing organizes the data in advance, allowing the main grammar creation process to proceed quickly while still achieving comprehensive coverage through the pre-prepared tagged data structure
Solution Approach 2:
The system segments the text processing task into distinct phases: tagging individual text strings, identifying substitutions, determining substring relationships, and generating grammar rules. This segmentation allows each phase to be optimized independently, achieving comprehensive grammar coverage through systematic processing while controlling overall processing time through efficient phase transitions
Data Source
AI summary
Creating and processing a natural language grammar set of data based on an input text string are disclosed. The method may include tagging the input text string, and examining, via a processor, the input text string for at least one first set of substitutions based on content of the input text string. The method may also include determining whether the input text string is a substring of a previously tagged input text string by comparing the input text string to a previously tagged input text string, such that the substring determination operation determines whether the input text string is wholly included in the previously tagged input text string.