Artificial Document Generation with Metadata Ratio Override
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Solution Overview
Problem
Current artificial data generation methods struggle to effectively mimic the structure and content of source documents across various domains, such as finance and medicine, while maintaining the ratios and dependencies between terms, which limits their applicability in generating realistic and context-specific synthetic data.
Innovation Solution
The system employs a processor-based method that derives metadata from source documents, trains a model to output artificial documents, and overrides generated words to retain the ratios and dependencies, using techniques like recurrent neural networks and n-gram applications to ensure the artificial documents mimic the source documents' characteristics, including sentence structures and grammatical guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If current artificial data generation methods are used, then data generation can be performed, but the structure and content of source documents cannot be effectively mimicked while maintaining ratios and dependencies between terms
Solution Approach 1:
The system segments the document generation process into distinct components: metadata extraction from source documents, dictionary and n-gram generation for domain-specific terminology, neural network model training, and controlled text generation. This segmentation allows each component to be optimized independently, improving both mimicking accuracy and domain adaptability.
Solution Approach 2:
The system changes parameters by deriving metadata parameters (such as term ratios, sentence structures, and formatting characteristics) from source documents and using these as controlled parameters during artificial document generation. This ensures the generated documents maintain the same statistical properties and dependencies as the source documents across different domains.
2Productivity
If a neural network model generates artificial documents, then document generation speed increases, but the ratios and dependencies between terms are not maintained
Solution Approach 1:
The system implements feedback mechanisms where the neural network model is trained on metadata extracted from source documents, including term ratios and n-grams. During generation, the model receives feedback through the loss function that measures deviation from target ratios and dependencies, continuously adjusting predictions to maintain accuracy while preserving statistical properties.
Solution Approach 2:
The system performs preliminary actions by extracting metadata, generating dictionaries and n-grams, and training the neural network model before actual document generation. This pre-processing establishes the statistical framework and term relationships that the model will maintain during high-speed generation, ensuring both speed and precision.
3Manufacturing precision
If metadata parameters are derived from source documents and used to control generation, then document realism improves, but system complexity increases
Solution Approach 1:
The system extracts only the essential metadata parameters needed for realistic document generation, such as term frequency ratios, n-gram sequences, sentence structure patterns, and formatting characteristics. By taking out only these critical features rather than copying entire source documents, the system achieves high document realism while managing complexity through selective feature extraction.
Data Source
AI summary
An artificial document generation method generating a larger set of artificial documents which mimics a smaller set of source documents. The method may include deriving metadata parameters from source documents, each parameter characterizing the source documents and having more than one possible value. The deriving may include: determining which ratio of the source documents has each of the values, defining metadata ratios characteristic of the source documents; training a model, on the source documents, to output artificial documents; and running the model as trained thereby to output artificial documents and overriding at least some words generated by the model in the draft artificial documents. Overriding is configured to ensure that at least some of the ratios characteristic of the smaller set of source documents are retained in the larger set.