Error Correction via Dictionary Tree Probability and Evaluation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current error correction methods in interactive devices fail to accurately correct query requests due to incorrect input, leading to inefficient and inaccurate query results.
Innovation Solution
An error correction method that determines target candidate entities from a preset dictionary tree based on query requests, calculates probabilities, evaluates entities using attribution information, and selects the real intent entity by weighting probabilities and evaluation results, thereby improving error correction efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional error correction methods are used, then the query processing is simple, but the error correction accuracy is low
Solution Approach 1:
The error correction process is segmented into multiple independent stages: candidate entity determination, probability calculation, and evaluation. Each stage processes specific aspects of the correction task, allowing the system to achieve high accuracy through coordinated processing while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The system performs preliminary actions by pre-establishing a dictionary tree structure and pre-calculating probability models before actual error correction is needed. This preparation enables the system to quickly and accurately correct errors during runtime without requiring complex real-time computation, thus improving accuracy while controlling complexity.
2Measurement precision
If more candidate entities are evaluated, then the error correction accuracy improves, but the processing time increases
Solution Approach 1:
The system applies partial action by evaluating only the necessary subset of candidate entities rather than all possible entities. Through probability calculation and evaluation mechanisms, the system identifies and processes only the most relevant candidates, achieving high accuracy while reducing processing time compared to exhaustive evaluation.
Solution Approach 2:
The evaluation stage provides feedback on the quality and relevance of candidate entities, allowing the system to adjust its processing focus. This feedback mechanism enables the system to prioritize high-quality candidates and reduce processing time by avoiding unnecessary evaluation of low-quality entities.
3Reliability
If multiple algorithms are used for entity evaluation, then the correction reliability improves, but the computational cost increases
Solution Approach 1:
The system applies local quality by selecting and applying different algorithms or evaluation criteria tailored to specific local characteristics of the data and query context. Rather than uniformly applying all algorithms everywhere, the system adapts its approach to the specific requirements of each correction task, improving reliability while reducing unnecessary computational cost.
Solution Approach 2:
The system changes parameters such as the number of algorithms applied, evaluation depth, and candidate selection criteria based on the specific characteristics of the query and context. This dynamic parameter adjustment allows the system to maintain high reliability for critical corrections while reducing computational cost for less critical cases.
Data Source
AI summary
The present disclosure provides an error correction method. The error correction method includes: determining a plurality of target candidate entities from a preset dictionary tree based on a query request; for each target candidate entity, calculating a first probability that the target candidate entity is a legitimate entity; evaluating each target candidate entity to obtain an evaluation result, a target candidate entity corresponding to an evaluation result; and determining a real intent entity corresponding to the query request based on the first probability and the evaluation result.