论文标题
从结构范围中学习：通过混合图卷积网络改善方面级别的情感分析
Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional Networks
论文作者
论文摘要
方面级别的情绪分析旨在确定句子中对特定目标的情感极性。这项任务的主要挑战是有效地建模目标和情感之间的关系，以滤除与无关目标的嘈杂意见单词。最近的努力通过目标居住对或意见从单词级别或短语级别的角度掌握。基于以下观察结果：针对目标和情感基本上建立了短语词句句子结构的语法层次结构，希望利用全面的句法信息来更好地指导学习过程。因此，我们介绍了范围的概念，该概念概述了与特定目标相关的结构文本区域。为了共同学习结构范围并预测情感极性，我们提出了一个混合图卷积网络（HGCN），以合成从组成树和依赖树中综合信息，探索链接两种语法解析方法以丰富表示形式的潜力。四个公共数据集的实验结果表明，我们的HGCN模型的表现优于当前最新基准。
Aspect-level sentiment analysis aims to determine the sentiment polarity towards a specific target in a sentence. The main challenge of this task is to effectively model the relation between targets and sentiments so as to filter out noisy opinion words from irrelevant targets. Most recent efforts capture relations through target-sentiment pairs or opinion spans from a word-level or phrase-level perspective. Based on the observation that targets and sentiments essentially establish relations following the grammatical hierarchy of phrase-clause-sentence structure, it is hopeful to exploit comprehensive syntactic information for better guiding the learning process. Therefore, we introduce the concept of Scope, which outlines a structural text region related to a specific target. To jointly learn structural Scope and predict the sentiment polarity, we propose a hybrid graph convolutional network (HGCN) to synthesize information from constituency tree and dependency tree, exploring the potential of linking two syntax parsing methods to enrich the representation. Experimental results on four public datasets illustrate that our HGCN model outperforms current state-of-the-art baselines.
