Automated Content Generation via NLP and User Feedback
Solution Overview
Problem
Existing content generation technologies face challenges in accurately determining the meaning of information due to inaccuracies in back-end algorithms, leading to inefficient information retrieval.
Innovation Solution
A system and method that combines natural language processing with user feedback to generate and modify content metadata, incorporating context parameters and domain information, enabling real-time accurate metadata generation and improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If back-end algorithms are used to automatically extract content metadata, then automation is improved, but accuracy deteriorates
Solution Approach 1:
The system implements a feedback loop where users can review and correct automatically generated content metadata. The context analysis module receives user feedback about the accuracy of generated metadata and uses it to iteratively improve the algorithms, resolving the contradiction between automation and accuracy by allowing human oversight to refine machine-generated content.
Solution Approach 2:
The system dynamically adjusts the balance between automated generation and human review based on confidence levels and performance metrics. The context analysis module evolves the algorithms over time by incorporating user feedback, making the system adaptive and improving accuracy while maintaining high automation levels.
2Measurement precision
If context parameters are refined based on user feedback, then content accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the content analysis process into distinct modules: content analysis for initial metadata generation, context analysis for parameter refinement, and feedback processing for iterative improvement. This modular segmentation manages complexity by organizing functions into separate, manageable components that can be developed and maintained independently.
Data Source
AI summary
Described are computer-based methods and apparatuses, including computer program products, for automated content generation. In some examples, the method includes generating content metadata from document content via natural language processing based on one or more context parameters associated with the document content. The method can further include receiving user feedback about the content metadata from a computing device associated with a user associated with the document content. The method can further include modifying the one or more context parameters based on the received user feedback.