论文标题
通过封闭图形卷积网络和基于语法的法规改善基于方面的情感分析
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation
论文作者
论文摘要
基于方面的情感分析（ABSA）试图预测句子对特定方面的情感极性。最近，已经表明，依赖树可以集成到深度学习模型中，以产生ABSA的最新性能。但是，这些模型倾向于计算隐藏/表示向量而无需考虑方面术语，并且无法从可以从ABSA的依赖关系树中获得的单词的整体上下文重要性得分受益。在这项工作中，我们提出了一种基于图的新型深度学习模型，以克服ABSA先前工作的这两个问题。在我们的模型中，栅极向量是从该方面术语的表示向量生成的，以将基于图的模型的隐藏向量定制为方面术语。此外，我们提出了一种机制，可以根据依赖树在句子中获得每个单词的重要性得分，然后将其注入模型中，以改善ABSA的表示向量。所提出的模型在三个基准数据集上实现了最先进的性能。
Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect. Recently, it has been shown that dependency trees can be integrated into deep learning models to produce the state-of-the-art performance for ABSA. However, these models tend to compute the hidden/representation vectors without considering the aspect terms and fail to benefit from the overall contextual importance scores of the words that can be obtained from the dependency tree for ABSA. In this work, we propose a novel graph-based deep learning model to overcome these two issues of the prior work on ABSA. In our model, gate vectors are generated from the representation vectors of the aspect terms to customize the hidden vectors of the graph-based models toward the aspect terms. In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA. The proposed model achieves the state-of-the-art performance on three benchmark datasets.
