Automated Editorial Quality Assessment via ML Feature Extraction
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Solution Overview
Problem
Current automatic document quality/style assessment systems are limited by high costs and the need for human-graded training data, compromising their accuracy and scalability.
Innovation Solution
A computer-implemented system that automatically extracts features from initial and final versions of training documents to train a machine-learned classifier, which then evaluates the editorial quality of documents using a large and diverse feature set, eliminating the need for human evaluation and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hand-coded style/quality rules are included in the source code with human involvement for system development, then the system can be developed with controlled costs, but the accuracy of the system is compromised due to limited number of documents that can be graded
Solution Approach 1:
The patent replaces the mechanical process of manual rule coding and human grading with an automated machine learning system. The system automatically extracts features from document versions and trains classifiers without requiring manual intervention in rule creation, thereby enabling processing of large volumes of documents to improve accuracy while maintaining cost control through automation.
Solution Approach 2:
The system performs self-training by automatically extracting features from document versions and generating training data without human intervention. The machine learning models self-improve through automated processing of large document corpora, eliminating the need for costly manual grading while enhancing system accuracy through extensive data processing.
2Reliability
If a supervised learning approach with human-graded training data is used, then the system can learn from expert evaluations, but the process remains partially automated and costly due to significant human involvement
Solution Approach 1:
The patent substitutes human grading operations with automated feature extraction and machine learning classification. The system automatically compares document versions, extracts relevant features, and trains classifiers without requiring human graders, thereby achieving full automation while maintaining reliability through sophisticated automated analysis methods.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw document data and quality assessment results. These models automatically learn from document version comparisons and serve as the decision-making foundation, replacing direct human evaluation while maintaining assessment reliability through automated pattern recognition.
3Device complexity
If a limited set of features is used to emulate human grader criteria, then the system can be developed with fewer resources, but the system cannot capture various aspects of stylistic variation beyond coherence and fluency
Solution Approach 1:
The patent segments the document analysis into multiple independent feature extraction components, each capturing different aspects of document quality. The system extracts diverse features including grammatical correctness, formatting consistency, stylistic elements, and content coherence separately, then combines them for comprehensive assessment, thereby capturing various stylistic variations without overwhelming complexity.
Solution Approach 2:
The patent expands the feature space by introducing multiple dimensions of document analysis beyond traditional coherence and fluency metrics. The system incorporates additional dimensions such as grammatical features, formatting attributes, stylistic patterns, and version comparison metrics, enabling comprehensive capture of stylistic variations through high-dimensional feature representation.
Data Source
AI summary
A computer-implemented system and method for assessing the editorial quality of a textual unit (document, paragraph or sentence) is provided. The method includes generating a plurality of training-time feature vectors by automatically extracting features from first and last versions of training documents. The method also includes training a machine-learned classifier based on the plurality of training-time feature vectors. A run-time feature vector is generated for the textual unit to be assessed by automatically extracting features from the textual unit. The run-time feature vector is evaluated using the machine-learned classifier to provide an assessment of the editorial quality of the textual unit.