近日，实验室博士生徐广涛关于方面级情感三元组抽取的研究成果被期刊Information Fusion录用。Information Fusion为中科院一区期刊，影响因子14.8。

题目：Span-based Syntactic Feature Fusion for Aspect Sentiment Triplet Extraction（基于跨度的句法特征融合用于方面情感三元组抽取）

Abstract: Aspect sentiment triplet extraction (ASTE) is a particularly challenging subtask in aspect-based sentiment analysis. The span-based method is currently one of the mainstream solutions in this area. However, existing span-based methods focus only on semantic information, neglecting syntactic information, which has been proven effective in aspect-based sentiment classification. In this work, we combine syntactic information with the span-based method according to task characteristics and propose a span-based syntactic feature fusion (SSFF) model for ASTE. Firstly, we introduce part-of-speech information to assist span category prediction. Secondly, we introduce dependency distance information to assist sentiment polarity category prediction. By introducing the aforementioned syntactic information, the learning objectives of the first and second stages of the span-based method are clearly distinguished, thus effectively improving the performance of the span-based method. We conduct experiments on the widely used public dataset ASTE-V2. The experimental results demonstrate that SSFF significantly improves the performance of the span-based method and outperforms all baseline models, achieving new state-of-the-art performance.

摘要：方面情感三元组抽取（Aspect Sentiment Triplet Extraction，ASTE）是基于方面的情感分析中一个具有挑战性的子任务。基于跨度的方法是目前该领域的主流解决方案之一。然而，现有的基于跨度的方法仅关注语义信息，忽略了句法信息，而后者在基于方面的情感分类中已被证明是有效的。在本研究中，我们根据任务特性将句法信息与基于跨度的方法相结合，提出了一种用于ASTE的基于跨度的句法特征融合（Span-based Syntactic Feature Fusion, SSFF）模型。首先，我们引入词性信息以辅助跨度类别的预测；其次，我们引入依存距离信息以辅助情感极性类别的预测。通过引入上述句法信息，基于跨度方法的第一阶段和第二阶段的学习目标得以清晰区分，从而有效提升了该方法的性能。我们在广泛使用的公开数据集ASTE-V2上进行了实验，实验结果表明，SSFF显著提升了基于跨度方法的表现，并超越了所有基线模型，达到了新的SOTA（最先进）性能水平。