NLP Dataset Field Labeling for Ambiguous Names
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Solution Overview
Problem
Existing data processing systems struggle to efficiently and accurately assign labels to dataset fields, particularly when field names are ambiguous, use inconsistent naming conventions, and require metadata-driven processing without manual intervention.
Innovation Solution
A method and system that utilize natural language processing (NLP) to analyze field names and data content, generating or selecting field labels from a glossary, and optionally merging scores to ensure accurate label assignment, even when conventional methods fail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual label assignment is used to ensure accuracy, then label precision is improved, but processing time and labor cost increase significantly
Solution Approach 1:
The system enables self-service through automated label assignment using NLP and machine learning models that process field names and data samples independently, achieving both accuracy and efficiency without manual intervention for routine cases
Solution Approach 2:
The patent introduces an intermediary automated labeling system that sits between raw data and final labeled output, using trained models to translate field characteristics into appropriate labels, reducing both manual labor and errors
2Measurement precision
If comprehensive data analysis is performed to improve label accuracy, then measurement precision is improved, but computational resources and processing time increase
Solution Approach 1:
The system applies partial action by analyzing only necessary portions of data (field names and selective data samples) rather than complete datasets, achieving sufficient accuracy while reducing computational overhead through targeted analysis
Solution Approach 2:
The patent implements preliminary action through pre-trained machine learning models and pre-established label glossaries that are prepared in advance, enabling rapid inference during actual label assignment without performing exhaustive analysis from scratch
3Productivity
If automated labeling systems are implemented to reduce manual intervention, then productivity is improved, but measurement precision deteriorates due to ambiguity in field names
Solution Approach 1:
The system segments the labeling process into distinct stages: field name analysis, data sample analysis, candidate label generation, and final selection. This segmentation allows each component to specialize and improve overall accuracy while maintaining automation
Solution Approach 2:
The patent incorporates feedback mechanisms where the system evaluates candidate labels against multiple criteria (field name matching, data consistency, glossary constraints) and iteratively refines selections, with options for human feedback to improve future automated decisions
4Reliability
If multiple analysis methods are combined to handle ambiguous field names, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple analysis approaches (NLP-based field name analysis, statistical data analysis, pattern matching) into a unified automated labeling framework, achieving improved reliability through complementary methods while managing complexity through integrated architecture
Data Source
AI summary
Techniques for processing a dataset comprising data stored in fields to identify field labels. The field labels describe data stored in the dataset fields. The techniques determine whether any field labels in a field label glossary match a field. If none of the field labels in the field label glossary match the field, the techniques generate a new field label using the name of the field. The generated field label may be assigned to the field.