Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA). However, the trees obtained from off-the-shelf dependency parsers are static, and could be sub-optimal in ABSA. This is because the syntactic trees are not designed for capturing the interactions between opinion words and aspect words. In this work, we aim to shorten the distance between aspects and corresponding opinion words by learning an aspect-centric tree structure. The aspect and opinion words are expected to be closer along such tree structure compared to the standard dependency parse tree. The learning process allows the tree structure to adaptively correlate the aspect and opinion words, enabling us to better identify the polarity in the ABSA task. We conduct experiments on five aspect-based sentiment datasets, and the proposed model significantly outperforms recent strong baselines. Furthermore, our thorough analysis demonstrates the average distance between aspect and opinion words are shortened by at least 19% on the standard SemEval Restaurant14 dataset.

中文翻译：

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更接近：学习将方面与意见联系起来

依赖解析树有助于在基于方面的情感分析 (ABSA) 中发现意见词。然而，从现成的依赖解析器获得的树是静态的，在 ABSA 中可能不是最优的。这是因为句法树不是为捕获意见词和方面词之间的交互而设计的。在这项工作中，我们旨在通过学习以方面为中心的树结构来缩短方面与相应意见词之间的距离。与标准依赖解析树相比，aspect 和 opinion 词预计会更接近这种树结构。学习过程允许树结构自适应地关联方面和意见词，使我们能够更好地识别 ABSA 任务中的极性。我们对五个基于方面的情感数据集进行了实验，并且所提出的模型明显优于最近的强基线。此外，我们的全面分析表明，在标准 SemEval Restaurant14 数据集上，aspect 和 opinion 词之间的平均距离缩短了至少 19%。