Part-of-Speech Label Correction for Semantic Analysis
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Solution Overview
Problem
Existing part-of-speech labelling methods face challenges in accurately determining the variability of word parts of speech across different contexts, leading to inaccurate semantic analysis in natural language processing.
Innovation Solution
A method and device for semantic analysis that acquires sentence information, extracts and corrects illegitimate part-of-speech label sequences using legitimacy detection and transition probabilities, ensuring accurate output labels for improved semantic understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If part-of-speech labelling is performed using existing methods, then text data can be processed, but the accuracy of labelling is reduced due to illegitimate label sequences
Solution Approach 1:
The patent implements a feedback mechanism where the legitimacy of part-of-speech label sequences is detected and used to guide correction. The system detects whether a label sequence is illegitimate, and if so, uses transition probabilities to correct it, creating a closed-loop system that continuously improves labelling accuracy through self-validation and self-correction.
Solution Approach 2:
The patent changes the parameter of label sequence legitimacy by introducing legitimacy detection and correction mechanisms. It transforms illegitimate label sequences into legitimate ones by adjusting the labels based on transition probabilities, thereby improving the overall quality and reliability of the part-of-speech labelling output.
2Productivity
If deep learning models are used for part-of-speech labelling, then processing capability is improved, but accuracy is reduced due to context variability
Solution Approach 1:
The patent introduces an intermediary legitimacy detection mechanism between the deep learning model's raw output and the final semantic analysis. This intermediary layer validates and corrects the label sequences generated by the deep learning model, ensuring that context variability does not compromise the accuracy of the final semantic analysis.
3Speed
If part-of-speech labelling is performed without legitimacy detection, then processing speed is maintained, but semantic analysis precision deteriorates
Solution Approach 1:
The patent applies preliminary action by performing legitimacy detection and correction on part-of-speech label sequences before they are used for semantic analysis. This preliminary validation step ensures that only legitimate, accurate label sequences proceed to semantic analysis, thereby maintaining high processing speed while ensuring precision.
Data Source
AI summary
At a terminal equipment side, sentence information received by the terminal equipment is acquired. A part-of-speech label sequence of text data in the sentence information for which part-of-speech labelling is to be performed is extracted. A detection result is acquired by detecting legitimacy of the part-of-speech label sequence. When the detection result indicates that the part-of-speech label sequence is illegitimate, the part-of-speech label sequence is corrected. A corrected part-of-speech label sequence is output as a result of performing part-of-speech labelling on the text data. Semantics corresponding to the sentence information is determined according to output sentence information with part-of-speech labels.