论文标题
使用图形注意力神经网络调查针对目标情绪分类的键入句法依赖性
Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network
论文作者
论文摘要
目标情感分类预测输入文本中给定目标提及的情感极性。主导方法采用神经网络来编码目标句子及其上下文之间的关系。最近，已经研究了图形神经网络以整合任务的依赖性语法，从而实现了最新的结果。但是，现有方法不考虑依赖性标签信息，这在直觉上很有用。为了解决问题，我们研究了一个新颖的关系图注意网络，该网络集成了键入的句法依赖性信息。标准基准测试的结果表明，我们的方法可以有效利用标签信息来改善目标情感分类性能。我们的最终模型大大优于最先进的基于语法的方法。
Targeted sentiment classification predicts the sentiment polarity on given target mentions in input texts. Dominant methods employ neural networks for encoding the input sentence and extracting relations between target mentions and their contexts. Recently, graph neural network has been investigated for integrating dependency syntax for the task, achieving the state-of-the-art results. However, existing methods do not consider dependency label information, which can be intuitively useful. To solve the problem, we investigate a novel relational graph attention network that integrates typed syntactic dependency information. Results on standard benchmarks show that our method can effectively leverage label information for improving targeted sentiment classification performances. Our final model significantly outperforms state-of-the-art syntax-based approaches.
