Semantic Classification Model Training Using Query Templates
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Solution Overview
Problem
Existing text classification models face challenges in achieving generality and effectively addressing sample imbalance issues, which affect their classification accuracy and efficiency in various scenes.
Innovation Solution
A classification model training method that involves constructing and training semantic classification models using pre-constructed templates, where sample query templates are inputted to obtain semantic categories, and the models are trained to reduce differences between sample and label categories, improving classification capability and adaptability across different scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional text classification models are used, then classification accuracy can be achieved in specific scenes, but the models lack generality and cannot effectively address sample imbalance issues across different scenes
Solution Approach 1:
The patent applies universality by designing a query template construction method that can handle multiple classification scenes and sample imbalance issues through a unified framework. The template-based approach allows the model to adapt to different classification tasks while maintaining consistent performance, making the system multi-functional rather than scene-specific
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting template parameters and semantic role labels based on the input query and classification task. This allows the model to adapt its internal representations to different scenes and data distributions, improving both generality and accuracy through flexible parameter transformation
2Productivity
If traditional classification models are trained with imbalanced samples, then the model can learn from available data, but the classification accuracy deteriorates for minority classes
Solution Approach 1:
The patent applies preliminary action by pre-constructing query templates and semantic role labels before training. This preprocessing step organizes the imbalanced data into a structured template format that highlights minority class patterns, allowing the model to learn more effectively from limited samples without requiring extensive retraining
Solution Approach 2:
The patent introduces query templates as an intermediary representation between the input text and the classification model. This intermediary structure helps bridge the gap caused by sample imbalance by providing a standardized framework that emphasizes important semantic roles, thereby improving minority class recognition while maintaining training efficiency
3Measurement precision
If more samples are collected to improve classification accuracy, then the model performance can be enhanced, but the complexity of data collection and processing increases
Solution Approach 1:
The patent applies segmentation by breaking down the classification task into template-based components and semantic role labels. This segmentation allows the model to learn from fewer, more structured samples rather than requiring large volumes of unprocessed data, reducing data collection complexity while maintaining accuracy through focused learning on key semantic elements
Data Source
AI summary
A semantic classification model training method includes that a sample query template and a label category of at least one category to be predicted in the sample query template are acquired, where the sample query template is constructed according to a sample query statement and a number of the at least one category to be predicted; the sample query template is input to the pre-constructed semantic classification model to obtain a sample semantic category of the at least one category to be predicted; and the semantic classification model is trained according to the sample semantic category and the label category of the at least one category to be predicted.