Dynamic NLP Item Prioritization via User Interaction Feedback
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Solution Overview
Problem
Users face overwhelming tasks when correcting natural language processing (NLP) errors in documents, such as medical records, due to the vast number of NLP items that need review, leading to frustration and potential avoidance of the correction and review system.
Innovation Solution
A system that detects user interactions with NLP items, determines context attribute values, and generates interaction information to prioritize NLP items based on frequency and context, allowing them to be presented to users in an order that reflects their importance, focusing on high-priority items first.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all NLP items are presented to users for review, then complete coverage of errors is achieved, but user frustration increases and completion rates decrease
Solution Approach 1:
The system implements feedback loops by monitoring user interactions with NLP items (views, corrections, time spent) and using this data to dynamically adjust the prioritization algorithm. This feedback mechanism allows the system to learn from user behavior patterns and continuously improve item ranking, ensuring that high-priority items are consistently presented first while adapting to changing user needs and document types.
Solution Approach 2:
The system changes the parameter of item presentation order from static (e.g., alphabetical or position-based) to dynamic prioritization based on multiple factors including error severity, user interaction history, and contextual relevance. By adjusting these prioritization parameters in real-time, the system optimizes both error detection completeness and user experience, preventing overwhelming users while maintaining thorough review coverage.
2Productivity
If NLP items are prioritized based on user interaction data, then user efficiency improves, but system complexity increases
Solution Approach 1:
The prioritization system is segmented into distinct functional modules: data collection module (gathering user interaction metrics), analysis module (processing interaction patterns), prioritization algorithm module (ranking NLP items), and presentation module (displaying items in optimized order). This segmentation allows each component to be developed, tested, and maintained independently, reducing overall system complexity while maintaining high productivity through coordinated module interactions.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing user interaction data in structured formats before prioritization is needed. Interaction metrics are collected and organized in advance, allowing the prioritization algorithm to quickly retrieve and process pre-prepared data rather than analyzing raw interaction streams in real-time, thus improving efficiency without proportionally increasing complexity.
3Measurement precision
If user interaction data is collected and processed, then item prioritization accuracy improves, but data processing time increases
Solution Approach 1:
The system implements periodic action by updating prioritization rankings at scheduled intervals (e.g., after a threshold number of user interactions or at fixed time intervals) rather than continuously reprocessing all data. This periodic update mechanism maintains high prioritization accuracy by incorporating recent user behavior patterns while avoiding the time cost of continuous real-time reprocessing, thus balancing precision with processing time efficiency.
Data Source
AI summary
Provided are a computer program product, system, and method for providing information on detected user interactions with natural language processor (NLP) items to a server to use to determine an order in which to render NLP items in a user interface. Interactions with natural language processing (NLP) items in documents are detected. For each interaction with an NLP item of the NLP items in the documents, determination is made of a context attribute value for a context attribute related to the interaction with the NLP item. Then interaction information is generated indicating the NLP item and the determined context attribute value. The interaction information is sent to the server to include in user interaction information in a database.