Automated Article Markup Generation via Entity Recognition

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Solution Overview

Problem

Current methods for generating markup information for training AI and machine learning models are time-consuming and prone to errors, leading to poor model performance.

Innovation Solution

A device and method that automatically generate article markup information using a processor to perform segmentation, named entity recognition, and expanded entity classification based on a preset expansion list, enabling accurate and efficient generation of markup information for model training.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual markup is performed one by one, then markup accuracy can be maintained, but time consumption increases and productivity decreases

Engineering Contradiction:

Improvemarkup accuracyVSAvoidmarkup generation speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent replaces the manual mechanical markup process with an automated computer-based system that uses named entity recognition models and expansion lists to generate markup information automatically, thereby eliminating the trade-off between manual accuracy and automation speed

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs self-service by automatically generating markup information through automated entity recognition and classification processes, eliminating the need for manual intervention while maintaining consistent accuracy through predefined expansion lists and recognition models

Inventive Principle:

Principle #25Self-service

2Reliability

If manual markup is performed, then markup information can be generated, but errors occur and reliability decreases

Engineering Contradiction:

Improvemarkup error rateVSAvoidtime for markup generation

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the error-prone manual markup process with an automated system that uses named entity recognition models and expansion lists to generate markup information consistently and accurately without human error

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms through the named entity recognition model that continuously processes segmentation results and generates markup information based on learned patterns from expansion lists, ensuring consistent and reliable output

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11954441B2Device and method for generating article markup information

Publication Date: 2024.04.09 ACER INC

AI summary

A device and method for generating article markup information are provided. The method for generating article markup information includes the following. Segmentation processing is performed on an article to generate a segmentation result. Name entity recognition is performed on the segmentation result to generate a first recognition result. Whether the segmentation result includes any word in an expansion list is determined. Expanded entity classification conversion is performed on the first recognition result to generate a second recognition result. The second recognition result and the segmentation result are used as markup information.