Text Coherence Classification Using Semantic Perplexity Scoring
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Solution Overview
Problem
Existing machine learning models generate multiword text strings with errors such as grammatical errors, misidentifications, and incoherent content, making them time-consuming to correct and discouraging operators from using automated summarizations.
Innovation Solution
A coherence classification system that utilizes a machine learning model to determine semantic probabilities and aggregate perplexity scores for multiword text strings, classifying coherence based on predefined conditions to ensure linguistic and semantic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models generate multiword text strings automatically, then productivity is improved, but manufacturing precision deteriorates due to grammatical errors and incoherent content
Solution Approach 1:
The system implements a feedback mechanism by calculating perplexity scores for generated text and using this information to identify and correct incoherent content. The perplexity calculation provides feedback about the quality of generated text, enabling the system to improve its output over time while maintaining automated generation capabilities.
Solution Approach 2:
The perplexity score acts as an intermediary metric between the machine learning model's generated text and the final quality assessment. This intermediary measurement allows the system to evaluate text coherence without requiring manual review, bridging the gap between automated generation and quality control.
2Loss of time
If machine learning models generate summaries quickly, then loss of time is reduced, but loss of information increases due to errors requiring correction
Solution Approach 1:
The system performs preliminary quality assessment by calculating perplexity scores immediately after text generation, before any manual correction is needed. This preliminary action identifies potential errors early, allowing for targeted corrections rather than comprehensive review, thus reducing both time loss and information loss.
3Manufacturing precision
If coherence classification is implemented to improve text quality, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system replaces complex manual coherence assessment with an automated computational approach using perplexity calculation. This substitution of mechanical/manual evaluation with an algorithmic system improves text quality assessment while avoiding the complexity of more sophisticated classification mechanisms.
4Measurement precision
If perplexity calculation is used to assess text coherence, then measurement precision is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system changes the parameter used for coherence measurement from subjective human evaluation to an objective computational metric (perplexity score). This parameter change enables precise measurement of text coherence while leveraging established natural language processing techniques that simplify the detection and measurement process.
Data Source
AI summary
Text coherence is classified by receiving a multiword text string into a machine learning model, determining, by the machine learning model, semantic probability data representing a probability that a word of the received multiword text string is semantically correlated to one or more other words in the multiword text string, determining, by the machine learning model, an inferential aggregate perplexity score of the multiword text string, based on the determined semantic probability data, outputting, from the machine learning model, the inferential aggregate perplexity score, and classifying a coherence of the multiword text string based on whether the outputted inferential aggregate perplexity score satisfies a coherence condition, wherein the coherence condition is based on a predefined coherence score.