Natural Language Parsing via Ontological Segmentation and Iterative Refinement
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Solution Overview
Problem
Current methods for transforming natural language expressions into formal language representations in computer-based information processing are limited by restrictive domains and input languages, failing to achieve the same level of accuracy as human parsing.
Innovation Solution
A method that partially parses natural language expressions by associating words with ontological entities, generating formal expressions with placeholder variables, filtering for consistency, and iteratively augmenting expressions to produce a unified formal representation suitable for downstream processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If severe restrictions are applied to domain or input language to achieve accurate parsing, then parsing accuracy is improved, but adaptability and versatility deteriorate
Solution Approach 1:
The parsing process is segmented into multiple stages: initial parsing, filtering, augmentation, and iterative refinement. Each stage handles specific aspects of the parsing task, allowing the system to manage complexity systematically while maintaining high accuracy across diverse domains and input languages without requiring severe restrictions
Solution Approach 2:
The system implements feedback mechanisms where parsed expressions are filtered for consistency and correctness, and incorrect or incomplete parses trigger iterative refinement. This feedback loop allows the system to self-correct and improve parsing accuracy while adapting to various domains and input languages without pre-defined restrictions
2Measurement precision
If multiple ontological entities are associated with each word or phrase, then parsing accuracy is improved, but device complexity increases
Solution Approach 1:
Ontological entities are pre-defined and organized in a hierarchical structure before parsing occurs. This preliminary organization allows the system to efficiently associate multiple entities with words or phrases during parsing without managing complexity in real-time, as the ontology framework is already in place to guide the association process
Solution Approach 2:
Different levels of ontological detail are applied locally based on the specific word or phrase being parsed. The system associates multiple ontological entities with words or phrases only when necessary and appropriate for the given context, rather than uniformly applying complex ontology management across all parsing operations, thus reducing overall system complexity while maintaining accuracy where needed
Data Source
AI summary
This invention comprises a series of steps which transforms one or more natural language expressions into a single, well-formed formal language representation. Each natural language expression is partially parsed into simple fragments, each of which is then associated with one or more short formal expressions. Each formal expression is constructed in such a way as to contain one or more placeholder variables, each of which is associated with one or more attributes to constrain the types of entities that each variable can potentially represent. The resulting plurality of formal expressions is then filtered for relevance within a given context, and the surviving expressions manipulated based upon a plurality of rules, which are cognizant of the attributes associated with each variable contained therein. A user is then presented with the resulting plurality of formal expressions, whereupon the user optionally selects, rejects, adds to, logically connects and otherwise manipulates each member of said plurality. When the user is satisfied that the plurality represents an intended meaning, the formal expressions are combined into a single, formal representation.