Document Auto-Classification via Label Propagation
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Solution Overview
Problem
Existing technologies face challenges in efficiently analyzing large data collections, particularly in identifying confidential or privileged documents and recommending relevant documents, due to the difficulty in tuning policies and rules, and the need for extensive training data, which is often unavailable.
Innovation Solution
A machine learning framework is implemented for auto-classification of documents, allowing labels to be propagated without relying on existing training materials, using a feature extraction engine, data modeling service, and label propagation to analyze and classify documents based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If policies and rules with search terms are used to identify documents of interest, then the analysis can be performed without training data, but it is very difficult to properly tune the policies and rules to achieve acceptable scope and accuracy
Solution Approach 1:
The system implements feedback by allowing users to review and correct auto-generated labels. User corrections are fed back into the label propagation algorithm to refine future classifications. This creates a continuous improvement loop where the system learns from user feedback without requiring traditional training datasets, thereby improving accuracy while maintaining ease of implementation.
Solution Approach 2:
The label propagation algorithm enables the system to automatically classify documents without requiring manual configuration of policies and rules. The system self-adjusts by propagating labels from seed documents through the document network based on similarity metrics, eliminating the need for difficult policy tuning while achieving acceptable accuracy through iterative refinement.
2Measurement precision
If a classifier is trained using existing tagged documents, then analysis accuracy can be improved, but it requires a large body of training documents that often do not exist or cannot be made available
Solution Approach 1:
The system performs preliminary action by manually tagging a small subset of seed documents before deploying the label propagation algorithm. These pre-tagged seed documents serve as the starting point for automatic label propagation across the entire document collection, enabling accurate classification without requiring large training datasets. This preliminary tagging of key documents initiates the automated classification process.
Solution Approach 2:
The label propagation algorithm acts as an intermediary between the small set of tagged seed documents and the large collection of unlabeled documents. It transfers labeling information from the seed documents to similar documents through the document network, effectively bridging the gap between limited training data and comprehensive document classification without requiring direct access to large training datasets.
3Productivity
If traditional classification approaches are used, then documents can be analyzed, but the sheer volume of data makes effective analysis increasingly difficult
Solution Approach 1:
The system segments the large document collection into manageable units by identifying seed documents and propagating labels through local neighborhoods in the document network. Instead of analyzing the entire collection at once, the label propagation algorithm processes documents in distributed iterations, dividing the complex task into smaller local classification problems that are then aggregated into global results, thereby improving efficiency while managing complexity.
Data Source
AI summary
Disclosed is an approach for performing auto-classification of documents. A machine learning framework is provided to analyze the document, where labels associated with certain documents can be propagated to other documents.