提出EEGCN模型，提升细粒度情感分析的语法特征提取效果
Leveraging Deep Neural Networks for Aspect-Based Sentiment Classification
- 设计边增强GCN保留语法图中关键信息
- 在四个数据集上准确率显著优于现有方法
- 适合自然语言处理与情感分析研究者参考
细粒度情感分析旨在实现更精确的情感判断。尽管图卷积网络（GCNs）常用于提取情感特征，但其直接用于句法特征提取时易丢失关键信息。本文提出一种新型边增强图卷积网络（EEGCN），通过保留特征完整性提升性能。结合双向长短期记忆网络（Bi-LSTM）与基于自注意力的Transformer进行文本编码，有效捕捉长距离依赖关系。采用双向图卷积网络（Bi-GCN）进行消息传递，以捕获实体间关联，并引入针对特定方面的事物掩码技术去除冗余信息。在四个基准数据集上的大量实验与消融研究显示，EEGCN显著提升细粒度情感分析性能，解决了句法特征提取中的信息丢失问题，推动了该领域方法论的发展。
原文摘要 · Abstract (English)
Aspect-based sentiment analysis seeks to determine sentiment with a high level of detail. While graph convolutional networks (GCNs) are commonly used for extracting sentiment features, their straightforward use in syntactic feature extraction can lead to a loss of crucial information. This paper presents a novel edge-enhanced GCN, called EEGCN, which improves performance by preserving feature integrity as it processes syntactic graphs. We incorporate a bidirectional long short-term memory (Bi-LSTM) network alongside a self-attention-based transformer for effective text encoding, ensuring the retention of long-range dependencies. A bidirectional GCN (Bi-GCN) with message passing then captures the relationships between entities, while an aspect-specific masking technique removes extraneous information. Extensive evaluations and ablation studies on four benchmark datasets show that EEGCN significantly enhances aspect-based sentiment analysis, overcoming issues with syntactic feature extraction and advancing the field's methodologies.
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