Entity Recognition System with User Feedback Loop
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text analytics systems face challenges in efficiently handling ambiguous terms and require extensive manual labeling and re-training, limiting their adaptability and relevance, especially in medical applications where user insights are not integrated.
Innovation Solution
A method and system for text analysis that includes a concept recognition module, a disambiguation module, and user feedback mechanism to extract, disambiguate, and validate concepts, eliminating the need for new training datasets and reducing developer dependency by using user feedback to continuously improve entity recognition and linking capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling and re-training are used to improve entity recognition accuracy, then the system's reliability improves, but the time and cost to update the system increases
Solution Approach 1:
The system implements a feedback mechanism where users can correct entity recognition errors directly in the interface. These user corrections are automatically captured and used to retrain the recognition model, creating a continuous improvement loop that maintains high accuracy without requiring manual re-labeling of datasets
Solution Approach 2:
The system performs self-improvement through automated retraining using user feedback. Instead of requiring developers to manually collect and label new data, the system automatically utilizes user corrections to update its own recognition capabilities, making the improvement process self-service oriented
2Reliability
If manual labeling of text is performed to train entity recognition systems, then the system's reliability improves, but the complexity of system maintenance increases
Solution Approach 1:
The system captures user feedback on entity recognition accuracy and automatically uses this feedback to retrain the model. This eliminates the need for complex manual processes where developers would need to collect, clean, and label new training data, thereby reducing maintenance complexity while maintaining high reliability
Solution Approach 2:
The system replaces the mechanical process of manual data labeling and developer retraining with an automated computational process. User feedback is automatically processed by the system to update its recognition models, substituting manual mechanical work with automated algorithms
3Adaptability or versatility
If the system is designed to be highly adaptable to new terms and contexts, then the system's versatility improves, but the difficulty of detecting and measuring performance deteriorates
Solution Approach 1:
The system uses user feedback as a direct measure of performance for new terms and contexts. When users correct recognition errors for unfamiliar terms, the system learns from these corrections and updates its understanding, making performance measurement straightforward through user validation rather than complex automated evaluation
Data Source
AI summary
According to one embodiment, a method for text analysis is provided. The method includes recognizing a concept. Recognizing a concept includes receiving a stream of text including a plurality of entities, and extracting at least one concept from the plurality of entities. The method also includes disambiguating the at least one extracted concept. Disambiguating the at least one extracted concept includes receiving the at least one extracted concept, and generating at least one disambiguated concept corresponding to the at least one extracted concept.