ML Entity Extraction from Semi-Structured Enterprise Documents
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Solution Overview
Problem
Conventional extraction algorithms for semi-structured documents suffer from low accuracy and require extensive customization and maintenance, especially when dealing with documents of unknown formats, leading to time-consuming and error-prone processes.
Innovation Solution
A machine learning-based system that extracts structured information from semi-structured enterprise documents by identifying candidate entities and computing probability scores using a trained model, which includes structural, token-specific, and entity-specific features, allowing for high accuracy and reduced maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional extraction algorithms (rule-based or template-based) are used, then the extraction process is simple to implement, but the extraction accuracy is low and requires extensive customization for different document formats
Solution Approach 1:
The patent replaces conventional rule-based and template-based extraction algorithms with a machine learning model. Instead of using fixed rules and templates that require manual customization, the system employs a trained ML model that automatically learns document structures and extracts entities with high accuracy across different document formats without requiring extensive custom implementation for each format.
2Measurement precision
If custom implementations are created for extracting information from unknown documents, then the extraction accuracy improves, but the implementation becomes difficult to build and maintain
Solution Approach 1:
The patent creates a universal machine learning model that can handle multiple document formats and unknown document structures through incremental training. The model is designed to be format-agnostic and can adapt to new document types by training on sample documents, eliminating the need to create separate custom implementations for each document format while maintaining high extraction accuracy.
3Productivity
If conventional extraction algorithms are used, then the system is easy to maintain, but the extraction process becomes time-consuming and error-prone when dealing with documents of multiple formats
Solution Approach 1:
The patent incorporates feedback mechanisms where the machine learning model learns from extraction results and continuously improves its performance. The system uses training data from various document formats to refine its extraction capabilities, reducing errors and improving reliability over time while maintaining high productivity through automated processing.
4Adaptability or versatility
If rule-based or template-based extraction is used, then the system requires less computational resources, but it cannot adapt to deviations in information position or unknown document formats
Solution Approach 1:
The patent performs preliminary training of the machine learning model on diverse document formats before deployment. This preliminary action allows the model to learn various document structures and information positions in advance, enabling it to adapt to unknown formats and deviations without requiring extensive computational resources during the actual extraction process, as the adaptation capability is already built into the trained model.
Data Source
AI summary
Embodiments of the present disclosure provide systems and methods for extracting entities from semi-structured enterprise documents. The method performed by a server system includes receiving an enterprise document in a semi-structured format. The method includes extracting document features from the enterprise document. The document features include structural, token-specific, and entity-specific features. Further, the method includes identifying candidate entities in the enterprise document based at least on a machine learning model which uses document features. The candidate entities include candidate tabular entities and candidate non-tabular entities. The method includes computing probability scores for the one or more tokens-corresponding to the candidate non-tabular entities and the candidate tabular entities, based at least on the machine learning model. The method includes extracting structured data from the enterprise document according to the candidate non-tabular and tabular entities based at least on the probability scores.