Application of Adaptive Neural Network Algorithm Model in English Text Analysis

Area of Science:

• Natural Language Processing
• Machine Learning
• Computational Linguistics

Background:

• Existing optimization neural network algorithms require efficient adaptive mechanisms.
• Hyperparameter configuration in deep learning models can be complex and time-consuming.
• Accurate analysis of English text coherence is crucial for automated writing systems.

Purpose of the Study:

• To introduce a computationally efficient adaptive mechanism (adaptive exponential decay rate) for neural network optimization.
• To develop a dual-encoder model integrating Transformer and Bi-GRU for enhanced sentence feature extraction.
• To propose an improved subgraph matching algorithm for quantitative analysis of English text coherence.

Main Methods:

• Implemented an adaptive exponential decay rate mechanism integrated with the Adadelta algorithm (AEDR-Adadelta).
• Utilized a dual-encoder architecture (Transformer encoder for context, Bi-GRU encoder for source sentence information) with a gated decoder.
• Employed an improved subgraph matching algorithm on sentence semantic graphs to identify and analyze coherence patterns.

Main Results:

• The AEDR-Adadelta algorithm dynamically and adaptively acquires learning rates, reducing hyperparameter configuration complexity.
• The proposed model demonstrated improved feature extraction and analysis capabilities through attention mechanisms.
• Experimental results show reduced training and test errors, improved classification accuracy, faster convergence, and better text generalization.

Conclusions:

• The adaptive mechanism significantly enhances neural network optimization and text analysis.
• The semantic coherence diagnosis model effectively captures coherence features in English texts.
• The developed model shows promise for improving English composition correction systems and aiding educators.