Neural Model Annotation Feedback Loop
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Solution Overview
Problem
Existing natural language processing systems face challenges in efficiently identifying objects and understanding natural language, particularly in processing diverse input data formats and maintaining accurate knowledge resources, due to limitations in training neural models with annotated data and re-training processes.
Innovation Solution
A system with an AI platform and tools, including an annotation manager, ML manager, and document manager, that subjects documents to semantic annotation, builds and re-trains neural models using initial and adjudicated annotations, enriches un-annotated documents with machine-generated annotations, and selectively amends the models based on adjudication feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural models are trained with annotated data to improve accuracy, then measurement precision improves, but loss of time increases due to the extensive annotation process
Solution Approach 1:
The system performs preliminary automated annotation using neural models before human adjudication, preparing draft annotations that reduce the time required for human reviewers to create accurate annotations from scratch
Solution Approach 2:
The system implements feedback loops where adjudicated annotations are used to re-train and improve neural models, continuously enhancing annotation accuracy while reducing the need for extensive manual annotation over time
2Adaptability or versatility
If diverse document formats are processed to improve adaptability, then versatility improves, but device complexity increases due to multiple processing requirements
Solution Approach 1:
The system employs universal neural models and standardized processing pipelines that can handle multiple document formats (PDF, DOCX, TXT, HTML) through a single unified architecture, avoiding the need for separate specialized processors for each format
Solution Approach 2:
The system introduces intermediate representation layers and standardized data structures that mediate between diverse input formats and the core processing logic, simplifying format handling while maintaining versatility
3Reliability
If manual adjudication is performed to improve annotation reliability, then reliability improves, but productivity decreases due to the time-consuming review process
Solution Approach 1:
The system performs preliminary automated annotation to generate draft annotations that are then reviewed by human adjudicators, significantly reducing the time and effort required for manual review while maintaining high reliability
Solution Approach 2:
The system applies partial automation where neural models handle the initial annotation generation, and human adjudicators focus only on reviewing and correcting specific portions, rather than performing complete manual annotation
4Measurement precision
If neural models are re-trained with adjudicated annotations to improve accuracy, then measurement precision improves, but loss of time increases due to the re-training process
Solution Approach 1:
The system implements periodic re-training schedules where neural models are re-trained at optimized intervals using accumulated adjudicated annotations, balancing accuracy improvements with time constraints rather than re-training continuously
Solution Approach 2:
The system performs preliminary evaluation of adjudicated annotations to identify high-value training samples, selecting only the most beneficial annotations for re-training to reduce the time and computational resources required
Data Source
AI summary
Embodiments relate to a system, program product, and method for knowledge resource management. A first document is subjected to a first semantic annotation and one or more entities, relations, and textual annotations of interest are identified. A neural model is built with the first document and trained with the first document and one or more of the first semantic annotations. An un-annotated document is applied to the neural model, and one or more second semantic annotations are produced. The un-annotated document is enriched with the produced second semantic annotation(s) and is subjected to adjudication. The neural model is selectively amended responsive to the adjudication.