Automatic Grammar Generation from Distributed Knowledge
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Solution Overview
Problem
Existing grammar generation systems are labor-intensive and limited in coverage, as they rely on manual creation of rules and are costly, making it challenging to recognize a large variety of phrases with high recognition accuracy, especially in natural human-robot interaction.
Innovation Solution
A computer-based system that automatically generates a grammar by receiving data from a distributed network, tagging parts of speech, identifying filler and core words, modeling sentence structure, and creating a Finite State Grammar Transducer (FSGT) using synonyms, enabling the mapping of utterances to task labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual creation of grammar rules is used, then grammar coverage can be improved through expert knowledge, but the process becomes very tedious and costly with high out-of-grammar rates
Solution Approach 1:
The system enables self-service by automatically generating grammar rules from distributed user contributions without requiring manual expert intervention. The automated grammar generation system processes user data, performs part-of-speech tagging, identifies sentence structures, and creates FSGT grammars autonomously, eliminating the tedious manual rule creation process while maintaining comprehensive coverage.
Solution Approach 2:
The system changes the parameter of grammar generation from manual expert-driven to automated data-driven. By transforming user contributions into structured grammar rules through automated processing, the system shifts from a time-intensive manual approach to an efficient automated approach that scales with user participation.
2Adaptability or versatility
If distributed network data is collected for grammar generation, then coverage and variety of recognized phrases improve, but data processing complexity increases
Solution Approach 1:
The system segments the complex data processing task into distinct modular components: data collection from distributed network, part-of-speech tagging, filler word identification, core word extraction, sentence structure modeling, and grammar rule generation. This segmentation allows each component to be processed independently and efficiently, managing complexity while handling diverse user contributions.
Solution Approach 2:
The system introduces intermediate processing steps between raw user data and final grammar rules. Part-of-speech tagging and sentence structure modeling act as intermediaries that transform unstructured user contributions into structured representations, simplifying the subsequent grammar generation process while maintaining comprehensive phrase coverage.
3Productivity
If automated grammar generation is implemented, then manual labor is reduced and scalability improves, but recognition accuracy may decrease without expert rule crafting
Solution Approach 1:
The system implements feedback mechanisms where user interactions and recognition results are continuously processed to refine and update grammar rules. The automated system learns from distributed user contributions over time, improving recognition accuracy while maintaining high productivity through continuous automated processing and adaptation.
Solution Approach 2:
The system achieves universality by creating a single automated grammar generation framework that handles multiple tasks: collecting data from distributed sources, performing linguistic analysis, modeling sentence structures, and generating grammar rules for various applications. This multi-functional approach maintains accuracy across diverse domains while preserving scalability and efficiency.
Data Source
AI summary
The invention includes a computer based system or method for automatically generating a grammar associated with a first task comprising the steps of: receiving first data representing the first task based from responses received from a distributed network; automatically tagging the first data into parts of speech to form first tagged data; identifying filler words and core words from said first tagged data; modeling sentence structure based upon said first tagged data using a first set of rules; identifying synonyms of said core words; and creating the grammar for the first task using said modeled sentence structure, first tagged data and said synonyms.