Automatic Formality Classification Using Contextual Linguistic Features
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Solution Overview
Problem
Existing methods for determining and adjusting the formality level of online text items are inaccurate due to reliance on hand-written rules and binary classification models, failing to provide users with effective feedback on stylistic changes.
Innovation Solution
A system and method for automatic formality classification and transformation using machine learning models that extract linguistic features and contextual information to determine and adjust the formality level of text items, allowing for real-valued formality ratings and transformation between informal and formal language without altering the literal meaning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hand-written rules are used to determine formality level, then the method is simple to implement, but the accuracy of formality determination deteriorates
Solution Approach 1:
The patent replaces hand-written rule-based systems with machine learning models that automatically learn formality patterns from data. The system uses trained classifiers (such as support vector machines, neural networks, or ensemble methods) to determine formality levels, substituting manual linguistic rule creation with automated statistical learning that achieves higher accuracy without sacrificing implementability.
Solution Approach 2:
The patent transitions from binary classification (formal/informal) to multi-level formality scoring (e.g., 1-5 scale or continuous values). This parameter change allows for more nuanced formality determination, capturing subtle stylistic variations that binary models miss, thereby improving measurement precision while maintaining system simplicity through standardized scoring mechanisms.
2Device complexity
If binary classification models are used for formality determination, then the model complexity is low, but the formality determination accuracy deteriorates
Solution Approach 1:
The patent extends the classification output from two categories (binary) to multiple dimensions by introducing continuous formality scores or multi-level ratings. This dimensional expansion allows the model to capture gradations in formality (e.g., very informal, informal, neutral, formal, very formal) while using relatively simple underlying classification algorithms, thus improving accuracy without proportionally increasing complexity.
Solution Approach 2:
The patent implements dynamic formality adjustment where the system can adapt formality levels based on contextual factors such as user preferences, communication context, and feedback. Rather than static binary labels, the system dynamically determines and adjusts formality scores, allowing for more accurate and context-sensitive formality assessment while maintaining manageable model complexity through modular architecture.
3Device complexity
If existing formality classification methods are used, then the system is simple, but the ability to provide formality transformation guidance deteriorates
Solution Approach 1:
The patent incorporates feedback mechanisms where the system provides users with formality level assessments and suggestions for adjustment. The system analyzes user preferences from feedback data and automatically adjusts formality levels in generated content. This feedback loop enables the system to guide users on how to change formality levels effectively, transforming the static classification system into an adaptive tool that improves both accuracy and user capability.
Solution Approach 2:
The patent introduces an intermediary transformation layer between content generation and user delivery. This intermediary component not only classifies formality but also provides transformation capabilities, suggesting alternative phrasings and adjustments. The intermediary acts as a bridge that enhances user understanding of formality nuances and guides appropriate stylistic modifications without requiring complex user intervention.
Data Source
AI summary
The present teaching relates to automatic formality classification and transformation of online text items. In one example, a request is received for determining a formality level of a text item in an online communication. One or more linguistic features are extracted from the text item. Contextual information with respect to the online communication is extracted. A formality level of the text item is determined based on the one or more linguistic features and the contextual information. The formality level represents a degree of formality of the text item. The formality level is provided as a response to the request.