Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. However, these models always need large-scale computing resources, and they also ignore explicit modeling of structure between sentiment elements. To address these challenges, we propose an opinion tree parsing model, aiming to parse all the sentiment elements from an opinion tree, which is much faster, and can explicitly reveal a more comprehensive and complete aspect-level sentiment structure. In particular, we first introduce a novel context-free opinion grammar to normalize the opinion tree structure. We then employ a neural chart-based opinion tree parser to fully explore the correlations among sentiment elements and parse them into an opinion tree structure. Extensive experiments show the superiority of our proposed model and the capacity of the opinion tree parser with the proposed context-free opinion grammar. More importantly, the results also prove that our model is much faster than previous models.

翻译：利用预训练生成模型提取情感元素近期在方面情感分析基准任务上取得了显著提升。然而，这些模型始终需要大规模计算资源，且忽略了情感元素间结构的显式建模。为应对这些挑战，我们提出一种见解树解析模型，旨在从见解树中解析所有情感元素。该模型解析速度更快，并能显式揭示更全面、更完整的方面级情感结构。具体而言，我们首先引入一种新颖的上下文无关见解文法以规范化见解树结构，随后采用基于神经图表法的见解树解析器充分探索情感元素间的关联性，并将其解析为见解树结构。大量实验表明了我们所提模型的优越性，以及结合所提上下文无关见解文法的见解树解析器的解析能力。更重要的是，实验结果还证明我们的模型较先前模型具有更快的解析速度。