Rules-Based Template Extraction for Unstructured Documents
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Solution Overview
Problem
Traditional machine learning models often suffer from overfitting when trained with sparse or non-diverse datasets, leading to reduced effectiveness in extracting and analyzing terms from unstructured documents.
Innovation Solution
The implementation of an informed machine learning approach that leverages knowledge from subject matter experts to customize feature vectors and rulesets, allowing for the development of a customizable AI platform that enables users to define and refine extraction and analysis rules without requiring programming expertise, thereby reducing overfitting and improving model adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional machine learning models are trained with sparse or non-diverse datasets, then training time and data requirements are reduced, but model accuracy and reliability deteriorate due to overfitting
Solution Approach 1:
The system performs preliminary actions by pre-defining extraction rulesets and analysis rulesets before training the machine learning model. These rulesets capture domain knowledge and constraints that guide the model learning process, allowing the model to achieve better accuracy with less training data by following pre-established guidelines rather than learning everything from scratch.
Solution Approach 2:
The extraction ruleset and analysis ruleset act as intermediaries between the training data and the machine learning model. These rulesets mediate the learning process by providing structured constraints and guidance, enabling the model to generalize better from sparse data by filtering and organizing information through domain-expert-defined rules.
2Extent of automation
If traditional machine learning models are used for term extraction, then automation is achieved, but manufacturing precision and measurement precision worsen due to overfitting on limited data
Solution Approach 1:
Extraction rulesets and analysis rulesets are defined in advance to establish precise extraction and analysis criteria before the automated process begins. These pre-defined rules ensure that the automated term extraction maintains high precision by following expert-defined guidelines rather than relying solely on pattern recognition from limited training examples.
Solution Approach 2:
The rulesets serve as intermediaries that bridge automation and precision. They translate domain expertise into structured rules that guide the automated extraction process, ensuring that automation does not sacrifice accuracy by introducing domain knowledge constraints into the automated workflow.
3Adaptability or versatility
If customizable AI platform with expert knowledge integration is implemented, then adaptability and reliability improve, but device complexity and ease of operation worsen due to requiring programming expertise
Solution Approach 1:
The system enables self-service by allowing users to define and customize extraction rulesets and analysis rulesets through graphical interfaces without requiring programming expertise. Users can independently configure the AI platform to suit their specific needs by selecting and modifying rules from predefined templates, making the system adaptable while maintaining ease of operation.
Solution Approach 2:
The platform provides universal functionality by offering a standardized set of extraction and analysis rulesets that can be applied across different domains and use cases. Users can leverage these multi-functional rulesets for various term extraction tasks without needing to program custom solutions, achieving high adaptability through a unified interface.
Data Source
AI summary
A user may markup the training documents to identify salient terms in a set of training unstructured documents. The system may automatically generate an extraction ruleset for each salient term that can be manually modified or edited by the user. The user may also provide analysis rulesets for each of the salient terms using, for example, a no-code graphical user interface. A machine learning model can be trained to automatically extract and analyze the salient terms based on feature vectors built from the extraction rulesets and/or analysis rulesets of the salient terms. After training, the system may import a set of unstructured documents for term extraction and analysis by the trained machine learning model. The system may generate a report, such as a PDF or an interactive graphical user interface, summarizing the results of the extracted and analyzed salient terms.