Semantic Analysis Quality Evaluation via Weighted Confidence
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Solution Overview
Problem
Existing semantic analysis evaluation methods, such as matching length algorithms and TF-IDF, fail to accurately assess the quality of analysis results due to their inability to consider the varying contributions of segmented terms and distinguish between noise and semanteme, leading to poor accuracy.
Innovation Solution
A method that determines weights of analysis texts based on preset configuration information, detects semantic confidence using a pattern matching algorithm, and determines analysis type and field matching confidence using classification models, to obtain comprehensive analysis quality data by synthesizing these factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional matching length algorithms or TF-IDF are used to evaluate semantic analysis results, then the evaluation process is simple, but the accuracy of evaluating analysis quality is poor
Solution Approach 1:
The evaluation system is segmented into multiple independent modules: a weight determination module that assigns different weights to segmented terms, a pattern matching module that detects semantic confidence, and a classification module that determines analysis types and field matching confidence. Each module processes specific aspects of the analysis result independently, then their results are synthesized to achieve comprehensive and accurate evaluation while maintaining manageable system complexity.
Solution Approach 2:
The system introduces multiple evaluation parameters beyond simple matching length: term weights reflecting varying contributions of different terms, semantic confidence scores from pattern matching, analysis type classifications, and field matching confidence. These multiple parameters are synthesized to compute comprehensive analysis quality data, transforming the single-dimensional evaluation into a multi-parameter assessment that significantly improves accuracy.
2Measurement precision
If multiple evaluation factors are synthesized to improve accuracy, then the evaluation accuracy improves, but the computational complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-determining weights for different analysis texts based on their expected contributions to semanteme, and pre-establishing pattern matching rules and classification models. These preliminary configurations are stored and reused during evaluation, avoiding redundant computations and reducing the computational burden when actual evaluation is performed.
Solution Approach 2:
The classification models and pattern matching algorithms automatically determine analysis types and field matching confidence without requiring manual intervention or complex external processing. The system self-evaluates the semantic confidence and analysis quality by synthesizing the weighted terms and pattern matching results, reducing the need for additional computational resources for manual assessment.
Data Source
AI summary
A method and an apparatus for processing a semantic analysis result based on AI are provided. With the method, the weight of the analysis text in the corresponding analysis result is determined according to the preset weight configuration information; the semantic confidence of the analysis result is detected via the pattern matching algorithm; the analysis type of the analysis texts is determined by the first classification model, and the field matching confidence of the analysis texts over the analysis result is determined by the second classification model; and then the analysis quality data of the analysis result is obtained according to the weights, the semantic confidence, the analysis type and the field matching confidence.