Neural Semantic Vector Extraction for Chinese NLP Accuracy
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Solution Overview
Problem
Existing natural language semantic extraction methods face challenges with low accuracy and high error rates due to forced splitting of meanings during Chinese word segmentation, which leads to loss of semantic descriptions.
Innovation Solution
A semantic extraction method and apparatus that query characters in a character library based on single-character granularity, using neural networks to generate semantic vectors and weight vectors, allowing for the selection of the most accurate semantic intention and key information without forced splitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If Chinese word segmentation is performed based on probability samples, then the natural language identification process is simplified, but the identification accuracy becomes unstable and low
Solution Approach 1:
The patent replaces the traditional mechanical word segmentation approach with a neural network-based semantic vector generation system. The neural network processes character sequences to generate semantic vectors that capture meaning without relying on fixed segmentation rules or probability samples, thereby achieving both ease of operation and high identification accuracy simultaneously
Solution Approach 2:
The patent changes the fundamental parameter of language processing from discrete word tokens to continuous semantic vectors. By transforming words into dense vector representations that capture semantic meaning, the system achieves more accurate and stable identification while maintaining operational simplicity through automated neural network processing
2Ease of manufacture
If regular expression matching is used for semantic extraction, then the extraction process is straightforward, but meanings of unmatched parts are ignored causing high error rates
Solution Approach 1:
The patent segments the semantic extraction process into multiple stages: character-level semantic vector generation, permutation and combination of vectors to form word-level representations, and semantic intention classification. This segmentation allows the system to process every character and maintain all possible meanings while organizing the complexity into manageable steps
Solution Approach 2:
The patent merges all possible semantic interpretations into a unified representation by generating semantic vectors for all character permutations and combinations. Instead of discarding unmatched parts, the system combines all possible meanings into a comprehensive semantic space, then uses classification to identify the intended meaning, ensuring no information is lost
Data Source
AI summary
A semantic extraction method based on a natural language is provided. In a character library based on a single-character granularity, a character of an input natural language is queried, to obtain first semantic vectors representing semantic descriptions of the character. Candidate semantic intentions corresponding to second semantic vectors and weight vectors corresponding to the candidate semantic intentions are obtained, wherein the second semantic vectors are obtained by using a neural network and correspond to different permutations and combinations of the first semantic vectors. A candidate semantic intention is selected as a final semantic intention of the natural language based on a preset condition, and key information is obtained corresponding to the final semantic intention according to a weight vector corresponding to the final semantic intention.