Context-Based NLP Model Chaining for Complex Document Annotation
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Solution Overview
Problem
Training and applying natural language processing (NLP) models require significant computational resources and time, especially when dealing with large volumes of natural language texts, and existing methods are inefficient in handling documents with varying complexities and semantic variations.
Innovation Solution
A system that employs multiple NLP models, each trained on different lengths of contextual information, iteratively applies these models to annotate natural language documents, utilizing contextual information from previous models to enhance prediction accuracy and reduce computational burden, and fine-tunes pre-trained models with domain-specific corpora to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single NLP model is used to annotate natural language documents, then the system is simple and fast, but the accuracy and precision are insufficient for documents with varying complexities
Solution Approach 1:
The patent divides the NLP annotation task into multiple specialized models, each responsible for specific provisions or document types. This segmentation allows each model to be optimized for its specific domain, improving overall accuracy while managing complexity through modular architecture where each model is independent and can be trained separately on domain-specific data.
Solution Approach 2:
The system dynamically selects and applies different NLP models based on the document type, provision class, and complexity level. This dynamic model selection enables the system to adapt to varying document characteristics, applying simple models to straightforward documents and complex models to challenging ones, thereby optimizing both accuracy and computational efficiency.
2Manufacturing precision
If multiple NLP models with varying contextual information lengths are applied to annotate documents, then the precision and recall improve, but the computational resources and time required increase significantly
Solution Approach 1:
The patent applies different contextual information lengths selectively based on the specific provision being annotated. For provisions requiring detailed contextual understanding, the system uses models with longer contextual windows. For simpler provisions, it uses models with shorter contextual windows, thereby optimizing computational resources by applying high computational cost only where necessary.
Solution Approach 2:
The system changes the parameter of contextual information length dynamically when selecting models. By adjusting this parameter based on document and provision characteristics, the system achieves high precision for complex provisions while maintaining computational efficiency for simpler ones, resolving the contradiction between precision and resource consumption.
3Reliability
If pre-trained NLP models are fine-tuned with domain-specific corpora, then the performance on specialized documents improves, but the training time and computational cost increase
Solution Approach 1:
The patent uses pre-trained NLP models as a foundation that have already been trained on large general corpora. This preliminary training captures universal language patterns and representations. The system then performs fine-tuning on domain-specific corpora to adapt these pre-trained models to specialized domains, leveraging the pre-trained knowledge to reduce the time and resources needed compared to training from scratch.
Solution Approach 2:
The system copies the architectural framework and pre-trained weights from general-purpose NLP models and adapts them to domain-specific tasks through fine-tuning. This copying approach allows the system to inherit the computational efficiency and performance of established models while customizing them for specific domains, thereby improving reliability without requiring complete retraining.
4Productivity
If the system processes a large number of natural language texts, then the productivity increases, but the computational burden and time consumption increase proportionally
Solution Approach 1:
The patent applies partial action by using NLP models with varying levels of computational complexity depending on the provision type. For straightforward provisions, the system uses simpler, faster models. For complex provisions requiring higher accuracy, it employs more computationally intensive models. This partial application of computational resources based on actual needs enables high processing throughput while managing overall computational power consumption.
Data Source
AI summary
Approaches for training and invoking natural language processing (NLP) models are described. In one aspect, a method includes obtaining a natural language document and accessing a database that includes NLP models. A first set of NLP models are configured to recognize a first provision. A second set of NLP models are configured to recognize a second provision. The method includes obtaining a user input indicative of a set of provisions. For at least one given provision in the set of provisions, the method include identifying corresponding NLP models in the database, applying a first NLP model to create a first annotated natural language document; inputting the first annotated natural language document to a second NLP model; and outputting the natural language document annotated with the set of provisions.