Adaptive Deterministic Classifier for Data Extraction
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Solution Overview
Problem
Conventional systems for extracting structured data from unstructured or semi-structured data face challenges such as limited ability to generalize to unseen data, high manual effort for data labeling, and low precision and recall rates, making them unsuitable for sensitive information extraction tasks.
Innovation Solution
A processing system that identifies expected relation classes, assimilates and populates named entity data, generates training data, and retunes classifier clusters using convergence techniques to improve precision and recall, enabling automated extraction of structured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If probabilistic classifiers (naïve Bayes, CRF) are used for data extraction, then the system can process unstructured data, but the precision and recall rates remain low and the system cannot generalize to unseen data
Solution Approach 1:
The system segments the data extraction process into multiple stages: initial probabilistic classification followed by deterministic refinement. By dividing the extraction task into separate phases (probabilistic filtering then deterministic verification), the system achieves both generalization capability and high precision/recall rates that single-stage classifiers cannot achieve alone.
Solution Approach 2:
The system performs preliminary probabilistic classification to identify candidate entities and relationships before applying deterministic rules for verification. This preliminary action narrows down the search space and allows subsequent deterministic processing to focus only on high-probability candidates, improving both generalization and accuracy.
2Measurement precision
If rule-based systems are used for data extraction, then the system can achieve high precision on known patterns, but it cannot generalize to unseen data or different business domains
Solution Approach 1:
The system dynamically adapts the extraction approach based on data characteristics. Probabilistic classifiers provide flexibility for unseen patterns while deterministic rules ensure precision for known patterns. The system dynamically switches between or combines these approaches depending on the confidence levels and data context, achieving both accuracy and generalization.
Solution Approach 2:
The system creates a multi-functional extraction framework that incorporates both probabilistic classification (for generalization) and deterministic rule-based processing (for precision). This universal system can handle both seen and unseen data patterns by leveraging the strengths of both approaches within a single integrated architecture.
3Measurement precision
If manual labeling is performed to generate training data for probabilistic classifiers, then the training data quality improves, but the time and effort required increases significantly
Solution Approach 1:
The system performs self-labeling by using deterministic rules to automatically generate labeled training data from unstructured sources. Instead of requiring manual annotation, the deterministic classification component automatically produces high-quality labeled data that can be used to train probabilistic classifiers, eliminating the time-consuming manual labeling process while maintaining data quality.
Solution Approach 2:
The system performs preliminary deterministic classification to generate initial labeled data before probabilistic training. This preliminary action creates a foundation of automatically labeled data that reduces or eliminates the need for manual annotation, significantly reducing the time and effort required for data preparation while ensuring sufficient training data quality.
Data Source
AI summary
This disclosure relates to initial learning of a classifier for automating extraction of structured data from unstructured or semi-structured data. In one embodiment, a method is disclosed, comprising: identifying at least one expected relation class associated with at least one expected relation data; populating at least one expected name entity data from the at least one identified expected relation class; generating training data by tagging the at least one expected relation data and the at least one identified expected relation class with unstructured or semi-structured data; generating feedback data for a relation data and relation class, using a convergence technique on the tagged training data; retuning a NE classifier cluster and a relation classifier cluster by continuously tagging new training data or generating new cascaded expression for a deterministic classifier and a statistical classifier; and extracting the structured data when the NE classifier cluster and the relation classifier cluster converge.