Synthetic Training Data Generation for NLP Models
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Solution Overview
Problem
Existing classification models in natural language processing require large amounts of manually labeled data, which is expensive and time-consuming, and often results in inconsistent labeling, leading to inefficiencies in data collection and model accuracy.
Innovation Solution
A system and method for generating natural language training data using sentence patterns with entity variables, where entities are sourced from external databases and randomly substituted to create diverse sentences for training statistical models like random forest and Naïve Bayes, enabling efficient data generation and model improvement through clustering techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data collection and labeling is used, then training data can be obtained, but the process is expensive and time-consuming
Solution Approach 1:
The patent uses template-based sentence generation to create synthetic training data by copying and substituting entities into predefined patterns. Instead of manually labeling each sentence, the system generates multiple variations by substituting different entities (people, places, organizations) into template structures, dramatically reducing labeling time while maintaining consistent labeling quality through automated generation.
Solution Approach 2:
The patent performs preliminary data preparation by collecting entities from external sources and organizing them into categorized lists before training. Entity extraction and validation are performed in advance, and training data is pre-generated using templates and substituted entities, so that the actual model training can proceed efficiently without time-consuming manual labeling during the training process.
2Productivity
If manual data labeling is performed by individuals, then training data is created, but labeling consistency and accuracy deteriorate
Solution Approach 1:
The system uses predefined sentence templates with structured placeholders for different entity types. By copying these templates and systematically substituting validated entities from external sources, the system ensures that all generated sentences follow consistent grammatical and semantic patterns, eliminating the variability introduced by manual labeling while maintaining high productivity through automated generation.
Solution Approach 2:
The patent incorporates entity validation mechanisms that check whether substituted entities match the required types and constraints defined in the templates. This feedback loop ensures that only valid entity substitutions are accepted, maintaining labeling consistency and data quality while enabling high-speed automated generation.
3Measurement precision
If large amounts of training data are collected, then model accuracy improves, but data collection costs increase
Solution Approach 1:
The patent generates large volumes of synthetic training data by copying template structures and substituting multiple entities into each template. A single template can generate numerous sentence variations by substituting different people, places, and organizations, thereby creating large-scale training datasets without the proportional cost increase of manual data collection.
Solution Approach 2:
The patent uses universal sentence templates that can accommodate multiple entity types and domains. The same template structure serves multiple purposes by substituting different entities from various external sources, allowing the system to generate diverse, domain-specific training data from a small set of reusable templates, reducing overall data collection requirements.
Data Source
AI summary
Provided is a system, method and computer-readable medium for generating data that may be used to train models for a natural language processing application. A system architect creates a plurality of sentence patterns that include entity variables and initiates sentence generation. Each entity is associated with one or more entity data sources. A language generator accepts the sentence patterns as inputs, and references the various entity sources to create a plurality of generated sentences. The generated sentences may be associated with a particular class and therefore used to train one or more statistical classification models and entity extraction models for associated models. The sentence generated process may be initiated and controlled using a user interface displayable on a computing device, the user interface in communication with the language generator module.