Question Importance Ranking for Scalable Intelligent Answering
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Solution Overview
Problem
In multi-user online intelligent question answering scenarios, existing systems struggle to efficiently process a large volume of questions, particularly when faced with meaningless or repetitive queries, leading to paralysis of the entire system due to insufficient processing capacity.
Innovation Solution
An intelligent question answering method that evaluates the importance of questions based on various influencing factors, determines the answering importance degree, and provides answers with varying degrees of detail, utilizing semantic clustering and network searching to dynamically generate responses, thereby prioritizing high-priority questions and optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all questions are answered indiscriminately in a retrieval-based intelligent question answering system, then completeness of answer coverage is improved, but system processing capability deteriorates and the system may become paralyzed
Solution Approach 1:
The patent applies dynamics by making the question answering system adaptive and flexible through importance evaluation. The system dynamically adjusts its processing behavior based on the evaluated importance of each question, transitioning between different processing modes (detailed answering vs. simplified handling) to optimize both completeness and processing capability under varying conditions
Solution Approach 2:
The patent changes the parameter of question processing by introducing an importance evaluation mechanism that assigns different importance degrees to questions based on multiple factors (user level, question category, frequency, etc.). This parameter change enables the system to differentiate between high-importance and low-importance questions, applying appropriate processing strategies to each to balance completeness and processing capability
2Measurement precision
If detailed answers are provided for all questions, then answer quality is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by providing different levels of answer detail based on the local characteristics of each question. High-importance questions receive detailed, high-quality answers with comprehensive information, while low-importance questions receive simplified or standardized responses. This localized quality adjustment optimizes answer quality for critical questions while reducing processing time for less important ones
3Ease of operation
If the system processes questions in strict chronological order, then fairness is improved, but responsiveness to important questions deteriorates
Solution Approach 1:
The patent applies preliminary action by evaluating the importance of questions before processing them. This pre-evaluation allows the system to prioritize high-importance questions and process them first, while still maintaining a systematic approach to handling all incoming questions. The importance evaluation acts as a preliminary sorting mechanism that prepares the question queue for optimized processing
Data Source
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AI summary
This application discloses an intelligent question answering method and apparatus, a computing device, a program product, and a storage medium, and belongs to the artificial intelligence field. The method includes: receiving an original question; determining an importance evaluation result of the original question based on the original question, where the importance evaluation result indicates an answering importance degree of the original question, and the importance evaluation result is determined based on at least one evaluation influencing factor; determining, based on the importance evaluation result, an answer corresponding to the original question in at least one candidate answer, where the at least one candidate answer has a different degree of detail; and outputting the answer corresponding to the original question. That is, in this application, importance of the original question is evaluated, and a degree of detail for answering the original question is determined based on the importance evaluation result, to flexibly answer the original question, avoid answering all questions indiscriminately, and further improve intelligent answering efficiency.