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.
翻译:外观型的精细见解提取(AFOE)旨在从审查中以对意见的对等形式或额外提取情绪极极化的三重观点形成三重观点。由于含有若干意见因素,完整的AFOE任务通常分为多个子任务,并在管道中实现。然而,在现实世界的情景中,管道方法很容易受到错误传播和不便的影响。为此,我们提出一个新的标签办法,即Grid tagging 计划(GTS),以端到端的方式处理AFOE的任务,但只有一种统一的网格标记任务。此外,我们设计了一个有效的GTS推断战略,利用不同意见因素之间的相互指示,以便进行更准确的提取。为了验证GTS的可行性和兼容性,我们分别根据CNN、BILSTM和BERT实施三个不同的GTS模型,并进行面向舆论对提取和三重见解提取数据集的实验。广泛的实验结果表明,GTS模型明显地超越了强大的基线,并实现了最先进的业绩。