Product reviews often contain a large number of implicit aspects and object-attribute co-existence cases. Unfortunately, many existing studies in Aspect-Based Sentiment Analysis (ABSA) have overlooked this issue, which can make it difficult to extract opinions comprehensively and fairly. In this paper, we propose a new task called Entity-Aspect-Opinion-Sentiment Quadruple Extraction (EASQE), which aims to hierarchically decompose aspect terms into entities and aspects to avoid information loss, non-exclusive annotations, and opinion misunderstandings in ABSA tasks. To facilitate research in this new task, we have constructed four datasets (Res14-EASQE, Res15-EASQE, Res16-EASQE, and Lap14-EASQE) based on the SemEval Restaurant and Laptop datasets. We have also proposed a novel two-stage sequence-tagging based Trigger-Opinion framework as the baseline for the EASQE task. Empirical evaluations show that our Trigger-Opinion framework can generate satisfactory EASQE results and can also be applied to other ABSA tasks, significantly outperforming state-of-the-art methods. We have made the four datasets and source code of Trigger-Opinion publicly available to facilitate further research in this area.

翻译：产品评论中通常包含大量隐式方面和对象-属性共存的情况。不幸的是，现有基于方面的情感分析（ABSA）研究大多忽视了这一问题，可能导致观点提取不够全面和公平。本文提出了一项名为“实体-方面-观点-情感四元组抽取”（EASQE）的新任务，旨在将方面术语分层分解为实体和方面，从而避免ABSA任务中的信息损失、非排他性标注以及观点误解。为促进这一新任务的研究，我们基于SemEval餐厅和笔记本电脑数据集构建了四个数据集（Res14-EASQE、Res15-EASQE、Res16-EASQE和Lap14-EASQE）。我们还提出了一种新颖的基于两阶段序列标注的触发-观点框架（Trigger-Opinion），作为EASQE任务的基线方法。实证评估表明，我们的触发-观点框架能够生成令人满意的EASQE结果，并且可应用于其他ABSA任务，显著优于现有最优方法。我们已公开这四个数据集及触发-观点框架的源代码，以促进该领域的进一步研究。