Hierarchical Document Classification for Automatic Model Selection
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Solution Overview
Problem
Conventional document processing and information extraction applications require manual selection of a model for each new document, creating a bottleneck in scaling the document processing and ingestion pipeline due to the lack of a single model capable of optimally processing all types of documents.
Innovation Solution
A method for automatically classifying heterogeneous documents using machine learning techniques, involving feature extraction, hierarchical classification, and model selection, which includes training machine learning models using user-annotated documents and human feedback to create classification hierarchies, allowing for the automatic selection of pre-trained models for document processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple pre-trained models are used to process different types of documents, then document processing capability is improved, but manual model selection complexity increases
Solution Approach 1:
The system performs self-service by automatically selecting the appropriate pre-trained model based on document features extracted through NLP processing. The model selection mechanism autonomously determines which model to use without requiring manual intervention, thereby maintaining high document processing capability while eliminating manual selection complexity.
Solution Approach 2:
The system changes parameters by analyzing document features (such as text length, structure, content type) and using these parameter variations to automatically select the most suitable pre-trained model. This parameter-based selection approach enables the system to adapt to different document types dynamically without manual intervention.
2Measurement precision
If manual model selection is required for each document, then processing accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training multiple models on different document types and pre-establishing selection criteria based on document features. When a new document arrives, the system quickly matches it against the pre-established criteria and selects the appropriate model, thereby maintaining high processing accuracy while significantly improving processing speed through automated decision-making.
3Device complexity
If a single model processes all document types, then system simplicity is improved, but processing quality deteriorates
Solution Approach 1:
The system achieves universality by creating a multi-functional framework where multiple pre-trained models work together under a unified automated selection mechanism. Each model specializes in specific document types, ensuring high processing quality, while the automated selection system maintains overall system simplicity by eliminating manual intervention and providing a seamless interface for document processing.
Data Source
AI summary
Methods, systems, and computer program products for automatically classifying heterogeneous documents using machine learning techniques are provided herein. A computer-implemented method includes extracting multiple features from one or more sets of heterogeneous documents; arranging at least a portion of the extracted features into multiple hierarchically-defined classes; selecting one or more of the documents associated with each of the multiple hierarchically-defined classes; training at least one machine learning model using at least a portion of the selected documents; and automatically classifying at least one input document into one or more of the multiple hierarchically-defined classes by processing one or more portions of the at least one input document using the at least one trained machine learning model.