Domain-Specific NLCs for Document Similarity Matching
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Solution Overview
Problem
Existing document similarity matching techniques are inefficient and require complex natural language understanding (NLU) annotation systems, manual sourcing of documents, and high processing power, making it difficult to accurately identify similar documents across different domains without understanding linguistics or building custom models.
Innovation Solution
A system using domain-trained natural language classifiers (NLCs) that parse documents, assign sentence confidence scores based on domain-specific taxonomy models, and generate document similarity data to identify relevant documents across domains, reducing false positives and processing power requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-trained natural language classifiers are used for document similarity matching, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the document similarity matching task into multiple domain-specific natural language classifiers, where each classifier is trained on a specific domain (e.g., legal, medical, technical). This segmentation allows each classifier to specialize in its domain, improving measurement precision for domain-specific documents while managing overall system complexity through modular architecture.
Solution Approach 2:
The system implements a universal framework that can handle multiple domains simultaneously through a collection of domain-trained classifiers. The framework provides multi-functionality by accommodating different domains (legal, medical, technical, etc.) within a single system architecture, allowing the same system to improve measurement precision across various document types without requiring separate systems for each domain.
2Measurement precision
If complex NLU annotation systems are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system replaces complex, hard-to-maintain NLU annotation systems with multiple simpler, domain-specific classifiers that can be independently trained and updated. Each domain classifier acts as a self-contained unit that is easier to operate and maintain, reducing the operational complexity while maintaining or improving measurement precision through domain specialization.
Solution Approach 2:
The domain-trained classifiers are designed to be self-sufficient units that can be trained on domain-specific data and deployed independently. This self-service architecture eliminates the need for complex centralized NLU annotation systems, making the overall system easier to operate as each classifier manages its own domain expertise without requiring intricate coordination between components.
3Measurement precision
If manual document sourcing is used, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-training multiple domain-specific natural language classifiers on domain-specific data before actual document similarity matching is needed. This preliminary training enables the classifiers to quickly and accurately identify similar documents without requiring manual sourcing at runtime, significantly reducing the time loss while maintaining high measurement precision through domain-specific expertise.
Solution Approach 2:
The system replaces the manual mechanical process of document sourcing with automated domain-trained classifiers that use natural language processing. This substitution eliminates the time-consuming manual search and comparison process, allowing the system to rapidly identify similar documents through automated classification based on domain-specific training, thereby reducing time loss while maintaining or improving identification accuracy.
4Measurement precision
If high processing power is used, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system segments the processing load across multiple specialized domain classifiers rather than using a single high-power general-purpose processor. Each domain classifier is optimized for its specific domain and can process documents more efficiently, reducing the overall processing power required while maintaining or improving measurement precision through domain-specific optimization.
Solution Approach 2:
The system changes the parameter of processing efficiency by using domain-trained classifiers that are optimized for their specific domains. These classifiers require less processing power compared to general-purpose high-power systems because they are specialized and can make faster, more accurate decisions with lower computational resources, thereby reducing energy consumption while maintaining high measurement precision.
Data Source
AI summary
In some examples, a document can be received and parsed to identify sentences of the document. A plurality of domain natural language classifiers (NLCs) trained based on domain training data associated with a respective domain of a plurality of domains can be programmed to classify each identified sentence to determine a sentence confidence score for each identified sentence. A plurality of document confidence scores for the document can be determined based on sentence confidence scores determined by the plurality of domain NLCs. Each document confidence score can characterize a relevance of the document to the respective domain of the plurality of domains. Document similarity data identifying at least one document among documents associated with a corresponding domain of the plurality of domains can be generated based on an evaluation of the document confidence scores for the given document and document confidence scores for the documents associated with the corresponding domain.