Named Entity Recognition Engine for Document Parameter Mapping
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Solution Overview
Problem
Conventional software systems lack a holistic customization framework, leading to inaccurate and inconsistent information sharing across corporate function units, resulting in inefficiencies and potential losses due to missed important dates and compliance requirements.
Innovation Solution
An Automation Platform with a named entity recognition engine (NER Engine) that extracts and maps named entities from documents to relevant parameters, allowing for prediction, user review, and fine-tuning of the recognition model to improve accuracy over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional software systems are used to manage corporate workflows, then basic document storage and task management are provided, but information accuracy and consistency across different functional units deteriorate
Solution Approach 1:
The patent replaces manual document review and parameter extraction processes with an automated Named Entity Recognition (NER) engine that uses machine learning to identify and extract named entities from unstructured document text. This substitution of mechanical human effort with an automated AI system improves information accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The NER engine is designed to automatically extract named entities and map them to document parameters without requiring manual intervention for each document. The system self-adjusts through continuous learning from user corrections, improving its accuracy over time while reducing the need for complex manual configuration and maintenance.
2Measurement precision
If manual review and annotation of document parameters is performed, then mapping accuracy is maintained, but processing time and productivity are reduced
Solution Approach 1:
The system implements a hybrid approach where the NER engine performs automatic named entity extraction for all documents, and manual review is applied only when the automated system's confidence score falls below a threshold. This partial automation maintains high mapping accuracy for most documents while significantly improving overall processing speed by minimizing manual intervention.
Solution Approach 2:
The system incorporates feedback loops where user corrections and annotations are continuously fed back into the NER model to refine and improve its accuracy. This allows the system to maintain high mapping precision over time while operating at automated speeds, as the model learns from corrections rather than requiring constant manual review.
3Measurement precision
If a customized NER model is implemented to improve document parameter extraction, then mapping accuracy is enhanced, but system complexity and computational resources increase
Solution Approach 1:
The NER model is designed as a dynamic, adaptive system that automatically adjusts its parameters and structure based on the specific document types and parameters it encounters. The model evolves through continuous training on organization-specific documents, allowing it to achieve high mapping accuracy for each organization's unique workflows without requiring complex manual configuration or maintenance of multiple static models.
Data Source
AI summary
Systems, methods and computer program products are presented for a named entity recognition engine. The NER Engine initiates extraction of named entities from a document(s) and identifies one or more required parameters that correspond to a document outline type classification(s) of the document(s). The NER Engine applies a named entity recognition model to the extracted named entities to predict respective mappings between the extracted named entities and the one or more required parameters, wherein the said mapping depends on a Previous Number of Words model which is based on a same predefined number of words that appear before a named entity, as well as a model based on the named entity being included in a document sentence, and a model which depends on position of the named entity in the document. The NER Engine generates a user interface for display of the predicted respective mappings.