Ontology-Driven Grammar Inference for NLU Systems
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Solution Overview
Problem
Developing natural language understanding (NLU) grammars is a time-consuming and costly process that requires human expertise, as it involves creating complex grammars to accommodate various expressions of ideas and commands, which is unfamiliar to software developers and affects the speed and accuracy of automatic speech recognition systems.
Innovation Solution
Infer a natural language grammar based on concept annotations according to an application ontology, allowing for the reuse of existing grammars and reducing the need for manual expertise by defining relationships between application-related concepts, and using a grammar inference module to create rules that promote low-level concepts to higher-level concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual grammar development is performed by human experts, then grammar quality and accuracy are improved, but development time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating initial grammar rules from application ontologies and concept annotations before human experts need to intervene. This pre-generation process creates a foundation that reduces the overall development time while maintaining quality through subsequent refinement.
Solution Approach 2:
The grammar development system serves itself by automatically inferring grammar rules from structured data sources (ontologies and annotations) without requiring constant human expert intervention. The system can generate, refine, and update grammars autonomously, reducing both time and cost while maintaining acceptable quality levels.
2Measurement precision
If complex grammars are created to accommodate various expressions, then recognition accuracy is improved, but system complexity and difficulty of development increase
Solution Approach 1:
The system segments complex grammar development into manageable components by breaking down expressions into structured elements based on application ontologies. Each concept and relationship in the ontology becomes a modular unit that can be independently defined and combined, reducing overall complexity while maintaining comprehensive recognition capability.
Solution Approach 2:
Application ontologies serve as intermediaries between natural language expressions and grammar rules. The ontology structure mediates the translation process, converting diverse user expressions into standardized conceptual representations, thereby simplifying grammar complexity while preserving recognition accuracy.
3Ease of manufacture
If traditional software development approaches are used for grammar creation, then programming skills are utilized, but grammar development effectiveness decreases due to unfamiliarity with non-procedural grammar syntax
Solution Approach 1:
The system replaces manual mechanical grammar writing with automated grammar inference mechanisms. Instead of requiring developers to manually craft grammar rules using unfamiliar syntax, the system automatically generates grammar rules from structured ontological data, making the process accessible to traditional software developers without requiring specialized grammar writing skills.
Solution Approach 2:
The system provides a universal approach to grammar development that works across different applications and domains by leveraging common ontology structures. This multi-functional methodology allows developers to use familiar software development practices for ontology creation, which then automatically translates into application-specific grammars, bridging the gap between general programming skills and specialized grammar development.
Data Source
AI summary
Inferring a natural language grammar is based on providing natural language understanding (NLU) data with concept annotations according to an application ontology characterizing a relationship structure between application-related concepts for a given NLU application. An application grammar is then inferred from the concept annotations and the application ontology.