Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA). However, some triplets extracted by ASTE are confusing, since the sentiment in a triplet extracted by ASTE is the sentiment that the sentence expresses toward the aspect term rather than the sentiment of the aspect term and opinion term pair. In this paper, we introduce a more fine-grained Aspect-Sentiment-Opinion Triplet Extraction (ASOTE) Task. ASOTE also extracts aspect term, sentiment and opinion term triplets. However, the sentiment in a triplet extracted by ASOTE is the sentiment of the aspect term and opinion term pair. We build four datasets for ASOTE based on several popular ABSA benchmarks. We propose two methods for ASOTE. The first method extracts the opinion terms of an aspect term and predicts the sentiments of the aspect term and opinion term pairs jointly with a unified tag schema. The second method is based on multiple instance learning, which is trained on ASTE datasets, but can also perform the ASOTE task. Experimental results on the four datasets demonstrate the effectiveness of our methods.

翻译：Aspetiment Sentiment Triplet Expliton(ASTE) 旨在从刑期中提取三重词语、情绪和见解三重词语,并试图为基于侧面情绪分析提供一个完整的解决方案。然而,ASTTE提取的一些三重词语令人困惑,因为ASTTE提取的三重词语是三重词语的情绪,因为ASTTE在三重词语中表达的情绪是该句对上面术语表达的情绪,而不是对上面术语和观点的情绪。在本文中,我们引入了一个更精细的Aspective-Sentiment-OpinExprition Expliton(ASOSOTE)任务。ASOTE还提取了方方面术语、情绪和见解三重词语三重词语的完整解决方案。然而,ASOTE提取的三重情绪是三重词语的情绪和观点。我们根据一些流行的ABSA基准为ATE构建了四个数据集。我们所训练的实验性任务也是以多种实例学习方法来展示AST。