Hypernym Corpus for Sparse Domain NLP Parsing
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Solution Overview
Problem
Current natural language processing (NLP) systems require large data science teams and extensive corpora, making them exclusive and inefficient for domains with sparse or non-existent data, particularly in complex query scenarios, leading to inadequate performance in real-time information retrieval.
Innovation Solution
A method and system for generating an indexed corpus using hypernyms, which combines syntactically and semantically equivalent tokens with domain-specific knowledge, allowing for comprehensive coverage in domains with sparse or non-existent corpora, by defining an input grammar, assembling tokens into hypernyms, and mapping them to semantic outputs for efficient query processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used for natural language processing, then query understanding accuracy is improved, but system complexity and resource requirements increase significantly
Solution Approach 1:
The patent pre-generates a comprehensive corpus of all possible query variations using grammar rules and hypernym relationships before deployment. This preliminary action eliminates the need for runtime machine learning model training and complex inference, thereby reducing system complexity while maintaining query understanding accuracy through pre-computed semantic mappings.
Solution Approach 2:
Instead of using complex machine learning models, the patent creates simplified copies of query semantics through hypernym-based token mappings. The system copies the essential meaning relationships from natural language into a structured corpus format, enabling accurate query processing without the computational overhead of machine learning systems.
2Measurement precision
If large corpora are used for training, then natural language understanding capability is improved, but data requirements and processing time increase
Solution Approach 1:
The patent creates a universal corpus structure using hypernym relationships that can handle multiple query types and domains with a single unified framework. This multi-functional approach eliminates the need for separate training corpora for different query patterns, reducing overall data requirements while maintaining comprehensive natural language understanding capability across diverse scenarios.
3Measurement precision
If domain-specific corpora are created, then query accuracy in specific domains is improved, but development cost and time increase
Solution Approach 1:
The patent designs a universal grammar-based corpus framework that can be applied to any domain without requiring domain-specific training data or extensive customization. The hypernym structure provides domain-agnostic semantic relationships that work across different contexts, eliminating the need for separate domain-specific corpus development while maintaining high query accuracy through grammatical rule matching.
4Measurement precision
If comprehensive grammatical coverage is achieved, then query parsing accuracy is improved, but corpus size and processing overhead increase
Solution Approach 1:
The patent pre-computes and stores all possible grammatical parse trees and semantic mappings in a structured corpus during system initialization. This preliminary generation of comprehensive grammatical coverage allows the runtime system to perform simple lookup operations rather than complex parsing, thereby maintaining high query parsing accuracy while reducing processing overhead during actual query execution.
Data Source
AI summary
Systems and methods of natural language processing in an environment with no existing corpus are disclosed. The method includes defining an input grammar specific to a chosen domain, the input grammar having a domain specific knowledge and general grammatical knowledge. Groups of tokens are identified within the input grammar having syntactic and semantic equivalence. The identified groups are assembled into hypernyms, wherein the hypernyms include a semantic output for each token in the hypernyms. A list of fields is then combined with the hypernyms for combination with the hypernyms. A corpus of possible combinations of hypernyms and fields is created. A data structure mapping each possible combination to a partial semantic output is generated and the data structure is saved for use in later processing.