Discourse Explainability via User Skill and Content Complexity Matching
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Solution Overview
Problem
Current natural language processing techniques superficially gauge foul language, sentiment, or dishonesty, failing to deeply understand the complexity and explainability of discourse, which limits their ability to resolve misunderstandings or prevent arguments.
Innovation Solution
A method that identifies user skill levels and content complexity, using natural language processing to determine an explanation level, generating relevant explanations based on the complexity and skill level, and providing them to users, incorporating techniques like Lexile analysis and machine learning for improved understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple lexical analysis methods are used to gauge foul language, sentiment, or dishonesty, then processing speed and computational efficiency are improved, but understanding depth and explainability of discourse deteriorate
Solution Approach 1:
The system segments discourse analysis into multiple levels: surface-level lexical analysis and deep-level semantic analysis. Each level processes different aspects of the discourse independently, allowing the system to maintain processing efficiency while achieving deeper understanding through combined analysis results.
Solution Approach 2:
The patent adds a new dimension to discourse analysis by introducing explainability metrics and contextual understanding layers beyond traditional sentiment analysis. This multi-dimensional approach enables the system to provide both quick assessments and deep explanations simultaneously.
2Use of energy by moving object
If traditional natural language processing techniques are used, then computational resource consumption is reduced, but ability to resolve misunderstandings and prevent arguments deteriorates
Solution Approach 1:
The system performs preliminary analysis using efficient lexical methods to identify potential issues, then selectively applies more computationally intensive deep analysis only when needed. This preliminary screening approach reduces overall computational resource consumption while maintaining the ability to resolve complex misunderstandings.
Solution Approach 2:
The patent introduces an intermediary layer that bridges simple lexical analysis and complex semantic understanding. This intermediary processing layer uses optimized algorithms to translate between different analysis depths, enabling the system to maintain low resource consumption while achieving high reliability in resolving misunderstandings.
3Device complexity
If shallow analysis methods are used, then system complexity is reduced, but ability to provide meaningful explanations deteriorates
Solution Approach 1:
The system segments the explanation generation process into modular components, each handling specific aspects of discourse analysis. This modular architecture maintains manageable system complexity while enabling comprehensive explanations through the integration of multiple specialized analysis modules.
Solution Approach 2:
The patent introduces intermediary processing layers that translate complex semantic analyses into human-understandable explanations. These intermediary layers act as mediators between the complex analysis engine and the user, preserving explainability information without requiring the entire system to be complex.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for explaining discourse is provided. The embodiment may include identifying one or more skill levels for one or more users. The embodiment may also include identifying a complexity level corresponding to a piece of content. The embodiment may further include determining an explanation level for the piece of content based on the complexity level of the piece of content and a target skill level from the one or more skill levels. The embodiment may also include generating an explanation for the piece of content according to the explanation level. The embodiment may further include providing the explanation to a target user.