Automated Text Quality Evaluation Using Reference Probability Comparison
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Solution Overview
Problem
Current methods for evaluating text quality in content creation, such as in online marketing and news, rely on subjective human evaluation, which is time-consuming, costly, and inconsistent, lacking objectivity and scalability.
Innovation Solution
An automated method that analyzes text quality by comparing mathematical characteristics of a text to be scored against those of a high-quality reference text using a transformation matrix, focusing on group probabilities and repetition values to provide an objective and language-independent quality score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual quality checking by selecting random contents is used, then quality evaluation can be performed, but the costs increase and the evaluation lacks objectivity
Solution Approach 1:
The patent replaces the mechanical manual evaluation system with an automated computational system. The quality evaluation is performed by a computer that calculates a quality index based on mathematical characteristics of the text, such as word frequency distribution and repetition patterns, eliminating the need for human evaluators and associated costs.
Solution Approach 2:
The patent transforms the subjective quality evaluation into an objective measurement by changing the evaluation parameters from human judgment to quantitative text characteristics. The quality index is computed from objective parameters such as the frequency of word repetitions and the distribution of word lengths, providing consistent and comparable results.
2Measurement precision
If a large number of contents are evaluated to reach conclusions, then evaluation accuracy improves, but the time and resources required increase
Solution Approach 1:
The patent extracts the essential characteristics of text quality from a small sample and uses these extracted features to evaluate the entire body of work. By analyzing a few representative contents and computing their mathematical characteristics, the system can generalize the quality assessment to all contents produced by a creator, avoiding the need to evaluate every single piece.
Solution Approach 2:
The patent creates a quality model based on a small sample of evaluated contents and uses this model (copy) to assess the quality of the entire corpus. The transformation matrix derived from sample data is applied to evaluate all contents, efficiently scaling the evaluation process.
3Adaptability or versatility
If subjective evaluation by individuals is used, then flexibility is maintained, but consistency across different evaluators is lost
Solution Approach 1:
The patent creates a universal evaluation system that applies the same mathematical criteria to all texts regardless of the evaluator or language. The quality index calculation method is language-independent and can be applied consistently across different creators, platforms, and time periods, ensuring evaluation consistency while maintaining flexibility through parameter adjustment.
Data Source
AI summary
Methods and processes evaluate a quality score of a text. The text includes a plurality of words. The methods compute first probability characteristics of groups of words in a reference text which is known to be a high-quality text. The methods also compute second probability characteristics of groups of words in a text to be scored. The methods also compute the quality score based on a difference between the first probability characteristics and the second probability characteristics.