Concept Disambiguation via User-Specific Metrics
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Solution Overview
Problem
Natural language processing systems face challenges in disambiguating ambiguous concepts, such as acronyms or words with multiple meanings, especially when insufficient context is available, leading to incorrect interpretation of messages.
Innovation Solution
The implementation of user-specific concept metrics and a concept embedding module that generates vector space models to rank concept candidates based on similarity scores, utilizing historical usage patterns and ontological relationships to disambiguate ambiguous concepts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NLP algorithms are used without user-specific context, then the system is simpler to implement, but the accuracy in disambiguating ambiguous concepts deteriorates
Solution Approach 1:
The system pre-computes user-specific concept metrics by analyzing historical messages and stores them for future use. This preliminary action allows the system to quickly retrieve and apply user context during disambiguation without performing complex real-time analysis, thereby improving accuracy while maintaining reasonable system complexity
Solution Approach 2:
The patent introduces user-specific concept metrics as an intermediary layer between the input message and the disambiguation process. These metrics serve as a mediator that captures user context and preferences, enabling more accurate concept disambiguation without requiring direct complex analysis of the entire message history during processing
2Measurement precision
If user-specific concept metrics are computed and stored, then the accuracy of concept disambiguation is improved, but the computational resources and storage requirements increase
Solution Approach 1:
The system extracts only the essential user-specific concept metrics from historical messages rather than storing entire message histories. This extraction process identifies and retains only the relevant conceptual information needed for disambiguation, reducing storage requirements while maintaining disambiguation accuracy
Solution Approach 2:
The patent transforms historical message data into condensed parameter representations (concept metrics) that capture the essential information in a compact form. By changing the data representation from raw messages to aggregated metrics, the system reduces storage requirements while preserving the information needed for accurate concept disambiguation
Data Source
AI summary
Techniques for concept disambiguation for natural language processing are described herein. An aspect includes receiving a message from a user. Another aspect includes identifying an ambiguous concept in the message. Another aspect includes determining a plurality of concept candidates corresponding to the ambiguous concept. Another aspect includes determining, for each of the plurality of concept candidates, a respective similarity score based on user-specific concept metrics corresponding to the user. Another aspect includes ranking the plurality of concept candidates based on the respective similarity scores. Another aspect includes determining that the ambiguous concept corresponds to a top-ranked concept candidate of the ranked plurality of concept candidates.