GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification
GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification
复制标题
DOI：
10.1016/j.eswa.2021.115712
复制
发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Xiaofei Zhu;Ling Zhu;Jiafeng Guo;Shangsong Liang;S. Dietze
中科院分区：
文献类型：
--
作者：
Xiaofei Zhu;Ling Zhu;Jiafeng Guo;Shangsong Liang;S. Dietze
基于语义的情感分类旨在识别句子对特定方面的情感极性，已成为情感分析的一项重要任务。现有的方法已经提出了有效的模型，并取得了令人满意的结果，但它们主要集中在利用一个给定的句子的局部结构信息，如局部性，顺序性或句子内的句法依赖约束。近年来，一些利用全局依赖信息的研究工作引起了人们越来越多的兴趣，并显着提高了文本分类的性能。本文将全局结构信息和局部结构信息同时引入到基于方面的情感分类任务中，提出了一种新的基于方面的情感分类方法，全局和局部依赖引导图卷积网络（GL-GCN）。特别是，我们利用句法依赖结构以及句子顺序信息（例如，BiLSTM的输出）来挖掘句子的局部结构信息。另一方面，我们使用整个语料库构建一个词-文档图来揭示词之间的全局依赖信息。此外，利用注意机制来有效地融合全局和局部依赖结构信号。在五个基准数据集上进行了大量的实验，结果表明，我们提出的框架优于基于方面的情感分类的最先进的方法。该模型使用PyTorch实现，并在GPU GeForce GTX 2080 Ti上进行训练。
Aspect-based sentiment classification, which aims at identifying the sentiment polarity of a sentence towards the specified aspect, has become a crucial task for sentiment analysis. Existing methods have proposed effective models and achieved satisfactory results, but they mainly focus on exploiting local structure information of a given sentence, such as locality, sequentiality or syntactical dependency constraints within the sentence. Recently, some research works, which utilizes global dependency information, has attracted increasing interest and significantly boosts the performance of text classification. In this paper, we simultaneously introduce both global structure information and local structure information into the task of aspect-based sentiment classification, and propose a novel aspect-based sentiment classification approach, i.e., Global and Local Dependency Guided Graph Convolutional Networks (GL-GCN). In particular, we exploit the syntactic dependency structure as well as sentence sequential information (e.g., the output of BiLSTM) to mine the local structure information of a sentence. On the other hand, we construct a word-document graph using the entire corpus to reveal the global dependency information between words. In addition, an attention mechanism is leveraged to effectively fuse both global and local dependency structure signals. Extensive experiments are conducted on five benchmark datasets in terms of both Accuracy and F1-Score, and the results illustrate that our proposed framework outperforms state-of-the-art methods for aspect-based sentiment classification. The model is implemented using PyTorch and is trained on GPU GeForce GTX 2080 Ti.