Semantic Domain Assignment Using Governance and Term Databases
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Solution Overview
Problem
Existing Natural Language Processing (NLP) systems struggle with accurately assigning semantic domains to incoming data, leading to suboptimal performance in downstream processing tasks.
Innovation Solution
A system and method that utilizes a domain engine to refine semantic domain assignments by referencing data governance domains and industry-specific terminology databases, filtering ambiguous terms, and clustering highly semantically loaded terms to improve data distribution across defined domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing NLP systems use basic semantic domain assignment methods, then the system complexity remains low, but the accuracy of domain assignment deteriorates
Solution Approach 1:
The system segments the domain assignment process into multiple stages: initial domain assignment, identification of highly semantically loaded terms, statistical distribution analysis, and iterative refinement. This segmentation allows the system to achieve high accuracy through a structured multi-step approach rather than a single complex operation.
Solution Approach 2:
The system performs preliminary actions by pre-identifying highly semantically loaded terms and pre-calculating their statistical distributions across domains before final domain assignment. This preliminary analysis enables more accurate domain assignment in subsequent steps without requiring complete re-analysis of all terms.
2Measurement precision
If the system analyzes distribution patterns of highly semantically loaded terms across all data, then the accuracy of domain assignment improves, but the processing time increases
Solution Approach 1:
The system extracts only the highly semantically loaded terms from the complete data corpus for detailed distribution analysis. By taking out and focusing only on these critical terms rather than analyzing every term equally, the system achieves accurate domain assignment while significantly reducing the volume of data requiring intensive processing.
Solution Approach 2:
The system applies different processing qualities to different terms: highly semantically loaded terms receive detailed statistical distribution analysis across all domains, while other terms receive standard processing. This local quality approach ensures high accuracy for critical terms without uniformly applying complex processing to all data, thereby reducing overall processing time.
3Measurement precision
If the system uses company-specific terminology and data governance domains, then the accuracy of semantic domain assignment improves, but the device complexity increases
Solution Approach 1:
The system uses company-specific terminology databases and data governance domain frameworks that serve multiple functions: they provide the basis for identifying highly semantically loaded terms, define the domain structure for assignment, and establish the vocabulary for statistical distribution analysis. This multi-functionality reduces the need for separate specialized components for each task.
Solution Approach 2:
The system introduces statistical distribution analysis as an intermediary mechanism that bridges company-specific terminology and final domain assignments. This intermediary layer processes and reconciles the relationship between specialized domain knowledge and data assignment, making the integration of company-specific information more manageable and less complex.
Data Source
AI summary
Embodiments relate to systems and methods that improve the definition of semantic domains within incoming data, and accurately distribute data over those defined domains. In a particular embodiment, company-specific terminology and data governance (d.g.) domains are used to define “highly semantically loaded” terms within an incoming linguistic data corpus having existing semantic domains assigned thereto. Analyzing distribution patterns of such highly semantically loaded terms across the incoming linguistic data (and/or across the d.g. domains) enhances the accuracy of assignment of semantical domains and distribution of the data across these domains. Such improved semantic domains can improve operation of computers tasked with downstream processing of the linguistic data—e.g., by Natural Language Processing (NLP).