GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification

计算机科学 依赖关系(UML) 情绪分析 判决 人工智能 依赖关系图 图形 利用 水准点(测量) 地点 依存语法 自然语言处理 理论计算机科学 哲学 语言学 地理 计算机安全 大地测量学
作者
Xiaofei Zhu,Liling Zhu,Jiafeng Guo,Shangsong Liang,Stefan Dietze
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:186: 115712-115712 被引量:42
标识
DOI:10.1016/j.eswa.2021.115712
摘要

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.
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