Natural Language Disambiguation via Smart Matching and Learning
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Solution Overview
Problem
Conventional natural language processing systems face difficulties in disambiguating human requests due to ambiguous entity names, requiring users to provide unambiguous language or frequent clarifications, which can be burdensome.
Innovation Solution
A system and method utilizing smart matching, confirmation, and machine learning to disambiguate natural language processing requests by matching entities based on user information, context, and history, reducing the need for user confirmations over time through a smart matching engine, confirmation engine, and learning engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional natural language processing systems request user clarifications for ambiguous inputs, then disambiguation accuracy is improved, but user burden and interaction complexity increase
Solution Approach 1:
The system performs self-learning by automatically analyzing user clarification responses and updating its own disambiguation models without requiring external retraining. The learning engine ingests clarification data and autonomously improves future disambiguation accuracy, allowing the system to serve itself while reducing user burden over time.
Solution Approach 2:
The system implements a feedback loop where user clarifications are captured, analyzed, and fed back into the learning engine to improve future disambiguation. The clarification history is stored and used to refine entity matching algorithms, creating a continuous improvement cycle that reduces the need for future clarifications.
2Reliability
If the system continuously requests confirmations for ambiguous requests, then disambiguation reliability is improved, but interaction efficiency and productivity deteriorate
Solution Approach 1:
The system dynamically adjusts its confirmation behavior based on learned confidence levels. As the learning engine improves disambiguation accuracy over time, the system becomes more confident in its entity matching and reduces confirmation requests accordingly. This dynamic adaptation maintains reliability while progressively improving interaction efficiency.
Solution Approach 2:
The system performs preliminary learning from clarification history before actual disambiguation decisions are made. By pre-processing and storing clarification patterns in advance, the system can make more accurate initial disambiguation decisions, reducing the need for subsequent confirmations and improving overall interaction efficiency.
3Device complexity
If the system uses simple entity matching without learning, then device complexity is reduced, but disambiguation accuracy and adaptability worsen
Solution Approach 1:
The learning system is segmented into distinct functional components: a clarification history storage module, a learning engine for processing clarification data, and an entity matching module that uses learned patterns. This segmentation allows the complex learning functionality to be added without overwhelming system complexity, as each component has a specific, manageable role.
Solution Approach 2:
The learning engine acts as an intermediary between user clarifications and the entity matching system. It processes raw clarification data, extracts meaningful patterns, and translates them into improved matching rules that the entity matching module can use. This intermediary layer manages complexity by providing a buffer between data input and processing.
Data Source
AI summary
A system and method is provided of disambiguating natural language processing requests based on smart matching, request confirmations that are used until ambiguities are resolved, and machine learning. Smart matching may match entities (e.g., contact names, place names, etc.) based on user information such as call logs, user preferences, etc. If multiple matches are found and disambiguation has not yet been learned by the system, the system may request that the user identify the intended entity. On the other hand, if disambiguation has been learned by the system, the system may execute the request without confirmations. The system may use a record of confirmations and/or other information to continuously learn a user's inputs in order to reduce ambiguities and no longer prompt for confirmations.