Semantic Clustering for Robot Query Response Accuracy
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Solution Overview
Problem
Chatbot systems face inefficiencies in providing accurate answers to user queries, as they often lack clear responses for certain questions, leading to low user interaction efficiency when users must sift through lists of similar answers.
Innovation Solution
A robot response method that extracts semantic information from user queries, matches it with pre-stored semantic information clusters, and determines the most frequently selected query questions to provide accurate responses, using neural networks and similarity calculations to identify relevant clusters and questions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the chatbot system stores a large number of Q&A instances to improve response coverage, then the system can handle more query types, but the system complexity and response time increase when users must sift through lists of similar answers
Solution Approach 1:
The patent segments the large number of Q&A instances into multiple semantic information clusters based on semantic similarity. Each cluster contains Q&A instances with similar semantic meanings, allowing the system to organize and manage large volumes of data efficiently without increasing overall system complexity
Solution Approach 2:
The patent performs preliminary clustering of Q&A instances by semantic information before actual query processing. This pre-organization into semantic clusters allows for faster retrieval and reduces the complexity of real-time matching operations
2Reliability
If the chatbot provides multiple similar answers to unclear queries, then the system maintains response availability, but user interaction efficiency decreases as users must sift through lists of similar answers
Solution Approach 1:
The patent uses user selection feedback to determine the target query question. When users select from presented options, this feedback is used to identify the most frequently selected query question as the target, enabling the system to learn and improve response accuracy over time
Solution Approach 2:
The patent changes the parameter of answer presentation from displaying multiple similar answers to determining a single target query question based on selection frequency. This parameter change improves user interaction efficiency by providing a definitive answer rather than requiring users to sift through multiple similar options
3Measurement precision
If the system uses semantic similarity matching to improve answer accuracy, then the response relevance improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the computational task of semantic matching by first clustering Q&A instances into semantic groups. This segmentation reduces the computational complexity by limiting the matching search space within clusters rather than across all Q&A instances
Solution Approach 2:
The patent performs preliminary semantic clustering of Q&A instances before actual query matching. This pre-computation of semantic relationships allows for faster and more accurate matching during actual user interactions without repeating the full computational process
Data Source
AI summary
The present application provides a robot response method, apparatus, device and storage medium. The method includes: obtaining, by a robot, current query voice; extracting semantic information of the current query voice; matching the semantic information of the current query voice with multiple semantic information clusters stored in advance to get a matched target semantic information cluster, where each semantic information cluster includes: at least one Q&A instance, and each Q&A instance includes: semantic information corresponding to a historical query voice and a query question selected in a query list corresponding to the historical query voice; and obtaining, by the robot, the number of times each query question was selected in the target semantic information cluster, determining, according to the number of times each query question was selected, a target query question corresponding to the current query voice, and outputting a query response corresponding to the target query question.