Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims at extracting aspect terms and opinion terms from review in the form of opinion pairs or additionally extracting sentiment polarity of aspect term to form opinion triplet. Because of containing several opinion factors, the complete AFOE task is usually divided into multiple subtasks and achieved in the pipeline. However, pipeline approaches easily suffer from error propagation and inconvenience in real-world scenarios. To this end, we propose a novel tagging scheme, Grid Tagging Scheme (GTS), to address the AFOE task in an end-to-end fashion only with one unified grid tagging task. Additionally, we design an effective inference strategy on GTS to exploit mutual indication between different opinion factors for more accurate extractions. To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets. Extensive experimental results indicate that GTS models outperform strong baselines significantly and achieve state-of-the-art performance.

该研究提出了一种基于Grid Tagging Scheme (GTS)的新型标签方案，用于将AFOE任务应用于端到端的一体化标签任务，并设计了一种有效的推理策略，以获取更准确的方面术语和意见术语抽取。实验结果表明，GTS模型在方面为导向的意见对抽取和意见三元组抽取方面均优于强基线，并实现了最先进的性能。

基于网格标记方案的面向方面的细粒度意见抽取