基于左-中-右分离神经网络的旋转注意情感分析
Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention
摘要：
近年来，深度学习技术在基于方面的情感分析中取得了成功。然而，有两个重要问题仍有待进一步研究，即，1)如何有效地表示目标，特别是当目标包含多个单词时; 2）如何利用目标和左/右上下文之间的交互来捕获其中最重要的单词。在本文中，我们提出了一种方法，称为左-中-右分离的旋转注意神经网络（LCR-Rot），以更好地解决这两个问题。我们的方法有两个特点：1）它有三个独立的LSTM，即，左、中、右三个LSTM，分别对应于一篇评论的三个部分（左语境、目标短语和右语境）; 2）它具有一个旋转的注意力机制，它模拟了目标和左/右语境之间的关系。target 2context注意力用于捕获左/右上下文中最具指示性的情感词。随后，使用context 2 target attention来捕获目标中最重要的词。这导致了目标的双侧表示：左意识目标和右意识目标。我们比较我们的方法在三个基准数据集与最近提出的十个相关方法。结果表明，我们的方法显着优于国家的最先进的技术。
英文摘要：
Deep learning techniques have achieved success in aspect-based sentiment analysis in recent years. However, there are two important issues that still remain to be further studied, i.e., 1) how to efficiently represent the target especially when the target contains multiple words; 2) how to utilize the interaction between target and left/right contexts to capture the most important words in them. In this paper, we propose an approach, called left-center-right separated neural network with rotatory attention (LCR-Rot), to better address the two problems. Our approach has two characteristics: 1) it has three separated LSTMs, i.e., left, center and right LSTMs, corresponding to three parts of a review (left context, target phrase and right context); 2) it has a rotatory attention mechanism which models the relation between target and left/right contexts. The target2context attention is used to capture the most indicative sentiment words in left/right contexts. Subsequently, the context2target attention is used to capture the most important word in the target. This leads to a two-side representation of the target: left-aware target and right-aware target. We compare our approach on three benchmark datasets with ten related methods proposed recently. The results show that our approach significantly outperforms the state-of-the-art techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
