Dynamic Bi-Elman Attention Networks (DBEAN): Dual-Directional Context-Aware Representation Learning for Enhanced Text Classification

• URL: http://arxiv.org/abs/2503.15469v2
• Date: Thu, 20 Mar 2025 10:09:43 GMT
• Title: Dynamic Bi-Elman Attention Networks (DBEAN): Dual-Directional Context-Aware Representation Learning for Enhanced Text Classification
• Authors: ZhengLin Lai, MengYao Liao, Dong Xu,
• Abstract summary: Traditional methods struggled with complex linguistic structures and semantic dependencies.<n>Deep learning has significantly advanced the field by enabling nuanced feature extraction and context-aware predictions.<n>This paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN), which integrates bidirectional temporal modelling with self-attention mechanisms.
• Score: 17.33216148544084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text classification, a fundamental task in natural language processing (NLP), aims to categorize textual data into predefined labels. Traditional methods struggled with complex linguistic structures and semantic dependencies. The advent of deep learning, particularly recurrent neural networks (RNNs) and Transformer-based models, has significantly advanced the field by enabling nuanced feature extraction and context-aware predictions. Despite improvements, existing models exhibit limitations in balancing interpretability, computational efficiency, and long-range contextual understanding. This paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN), which integrates bidirectional temporal modelling with self-attention mechanisms. DBEAN dynamically assigns weights to critical segments of input, improving contextual representation while maintaining computational efficiency.

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