Hybrid Tree and Neural Network Text Error Correction
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Solution Overview
Problem
The tree model is less efficient in long text error correction scenarios and requires re-training when modifying bad cases, affecting its original error correction capacity.
Innovation Solution
A method and device that combines a tree model with a deep neural network for error correction, where candidate segments are scored using both models, and user feedback is incorporated to improve context modeling and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a tree model is used for error correction, then feature combination capacity is improved, but efficiency in long text error correction deteriorates
Solution Approach 1:
The text is divided into multiple segments, and each segment is processed independently by the tree model. This segmentation approach allows the tree model to efficiently handle long texts by breaking them down into manageable parts, thereby improving processing efficiency while maintaining the model's feature combination capacity.
Solution Approach 2:
The patent combines the tree model with a deep neural network model to create a hybrid error correction system. The tree model handles feature combination while the deep neural network captures contextual information, merging the strengths of both models to improve overall efficiency and accuracy in long text error correction.
2Measurement precision
If the tree model is re-trained to modify bad cases, then error correction accuracy is improved, but original error correction capacity deteriorates
Solution Approach 1:
User feedback on correction results is collected and used to identify bad cases. The system uses this feedback to generate candidate corrections and score them using both the tree model and deep neural network, then applies corrections based on the scoring results without requiring full model re-training, thus maintaining original capacity while improving accuracy.
Solution Approach 2:
The system pre-processes text into segments and generates candidate corrections before final scoring. By preparing candidate segments and scoring them in advance using both models, the system can make accurate corrections without needing to re-train the tree model, preserving its original error correction capacity.
Data Source
AI summary
A method and device for correcting an error in a text are provided. The method includes: preprocessing the text to obtain at least one segment of the text; generating a plurality of candidate segments for the segment; scoring the plurality of candidate segments with a tree model, to obtain respective first scoring results of the plurality of candidate segments; scoring the plurality of candidate segments with a deep neural network model, to obtain respective second scoring results of the plurality of candidate segments; for each candidate segment, calculating a scoring of the candidate segment based on the first scoring result and the second scoring result of the candidate segment; ranking the plurality of candidate segments according to the scorings of the candidate segments, to obtain a ranking result; and correcting the error in the text according to the ranking result.