Abstract
This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction. The former task is to extract aspects of a product or service from an opinion document, and the latter is to identify the polarity expressed in the document about these extracted aspects. Most existing algorithms address them as two separate tasks and solve them one by one, or only perform one task, which can be complicated for real applications. In this paper, we treat these two tasks as two sequence labeling problems and propose a novel Dual crOss-sharEd RNN framework (DOER) to generate all aspect term-polarity pairs of the input sentence simultaneously. Specifically, DOER involves a dual recurrent neural network to extract the respective representation of each task, and a cross-shared unit to consider the relationship between them. Experimental results demonstrate that the proposed framework outperforms state-of-the-art baselines on three benchmark datasets.
Abstract (translated)
本文主要研究了基于方面的情绪分析的两个相关子任务,即方面词提取和方面情绪分类,我们称之为方面词极性共提取。前者的任务是从意见文档中提取产品或服务的各个方面,而后者是识别文档中表达的关于这些提取方面的极性。大多数现有的算法将它们处理为两个独立的任务,并逐个解决它们,或者只执行一个任务,这对于实际的应用程序来说可能很复杂。本文将这两个任务视为两个序列标记问题,提出了一种新的双交叉共享RNN框架(DOER),可以同时生成输入句的各个方面的项极性对。具体来说,实干者需要一个双循环神经网络来提取每个任务的各自表示,并需要一个交叉共享单元来考虑它们之间的关系。实验结果表明,该框架优于三个基准数据集的最新基线。
URL
https://arxiv.org/abs/1906.01794