In this work we investigate the capability of Graph Attention Network for extracting aspect and opinion terms. Aspect and opinion term extraction is posed as a token-level classification task akin to named entity recognition. We use the dependency tree of the input query as additional feature in a Graph Attention Network along with the token and part-of-speech features. We show that the dependency structure is a powerful feature that in the presence of a CRF layer substantially improves the performance and generates the best result on the commonly used datasets from SemEval 2014, 2015 and 2016. We experiment with additional layers like BiLSTM and Transformer in addition to the CRF layer. We also show that our approach works well in the presence of multiple aspects or sentiments in the same query and it is not necessary to modify the dependency tree based on a single aspect as was the original application for sentiment classification.

翻译：本研究探讨了图注意力网络在提取方面和意见术语方面的能力。我们将方面和意见术语提取视为类似于命名实体识别的词元级分类任务。在图注意力网络中，我们利用输入查询的依存树作为附加特征，并结合词元特征和词性特征。研究表明，依存结构是一个强大的特征，在CRF层的作用下能显著提升性能，并在SemEval 2014、2015和2016的常用数据集上取得了最佳结果。我们尝试在CRF层之外添加BiLSTM和Transformer等额外层。同时，我们的方法在同一个查询包含多个方面或情感时也能有效工作，且无需像情感分类的原始应用那样基于单个方面修改依存树。