Neural Network Semantic Classification via Representation Vector Splicing
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Solution Overview
Problem
Current methods for semantic classification in natural language processing struggle to effectively extract and utilize common and single representations from remarks to improve the objectivity and accuracy of analysis, particularly in contexts like medical forums where subjective texts are abundant and diverse.
Innovation Solution
A method involving neural networks, including generative and discriminative networks, and semantic classifiers, processes remarks to extract common and single representation vectors, which are then spliced and classified using softmax classifiers, enhancing the accuracy of semantic classification by distinguishing between intentions related to different remarked objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current semantic classification methods are used, then the classification process is simple, but the accuracy and objectivity of remark analysis deteriorates due to inability to effectively extract common and single representations
Solution Approach 1:
The patent segments the semantic representation into two distinct components: common representation (extracted by common representation extractor) that captures shared semantics across multiple remarks, and single representation (extracted by single representation extractor) that captures object-specific semantics. This segmentation allows the system to process and utilize different types of semantic information separately, improving classification accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent introduces common representation as an intermediary element that mediates between multiple single representations and the final classification. The common representation extractor processes individual remark representations to generate aggregated common representations, which then serve as additional input features for the semantic classifier. This intermediary layer enables the system to leverage shared semantic patterns across multiple remarks without directly complicating the classification process
2Reliability
If multiple representation extractors are introduced to extract common and single representations, then the accuracy of remark analysis improves, but the device complexity increases
Solution Approach 1:
The patent designs the common representation extractor and single representation extractor as universal components that can process different types of input remarks and generate corresponding representation vectors. These extractors serve multiple functions: extracting semantic features from individual remarks, aggregating common patterns across remarks, and providing structured input for classification. This multi-functionality reduces the need for separate specialized components for each task, thereby improving reliability without proportionally increasing complexity
Data Source
AI summary
Provided are a semantic classification method and apparatus, a neural network training method and apparatus and a storage medium. The semantic classification method includes: inputting a first remark relating to a first object; extracting a first common representation vector for representing a common representation in the first remark by processing the first remark using a common representation extractor; extracting a first single representation vector for representing a single representation in the first remark by processing the first remark using a first representation extractor; obtaining a first representation vector by splicing the first common representation vector and the first single representation vector; and obtaining a semantic classification of the first remark by processing the first representation vector using a first semantic classifier; where the common representation includes an intention representation which is used to remark on both the first object and a second object, the second object is an associated remarked object different from the first object, and the single representation in the first remark includes an intention representation which is only used to remark on the first object.