摘要：

方面情感三元组抽取是方面级情感分析中的一个重要子任务，旨在从句子中抽取方面词、意见词和情感极性。近年来，句法依赖树结合图卷积网络(GCN)已经在三元组抽取任务中取得了良好的效果。然而，这些方法大多没有充分利用语言特征，也没有对语言特征进行增强，且大部分忽略了全局上下文核心信息。因此，提出一种基于语言特征增强的方面情感三元组抽取模型LFE。首先，引入关键词的词性特征以充分利用语义信息；接着，考虑句法依赖类型，计算词间的相对句法依赖距离，使词能够关注离它较近的词的句法特征；然后，采用双仿射注意力机制结合GCN来增强语义和句法特征，GCN及双仿射注意力机制能有效地利用句法依赖树的结构信息，并将其融入模型中；最后，对全局特征与语言特征进行融合，以确保全局上下文中的关键信息不被忽略，从而提高模型的鲁棒性。实验结果表明，LFE模型在Res14、Lap14、Res15、Res16等4个数据集上的F1值相对GCN-EGTS-BERT模型分别提高了3.52、5.32、1.97、2.63百分点，证明其具有可行性和有效性。

关键词: 方面情感三元组抽取, 语言特征, 关键词词性, 相对句法依赖距离, 图卷积网络

Abstract:

Aspect sentiment triplet extraction is an important subtask in aspect-level sentiment analysis aimed at extracting aspect words, opinion words, and sentiment polarity from sentences. In recent years, the combination of syntactic dependency trees and Graph Convolutional Networks (GCN) has achieved satisfactory results in triplet extraction tasks. However, most of these methods do not fully utilize or enhance language features, and ignore global contextual core information. Therefore, an aspect sentiment triplet extraction model named Linguistic Feature Enhancement (LFE) based on language feature enhancement is proposed. First, the part-of-speech features of keywords are introduced to fully utilize semantic information; then, the syntactic dependency types are considered and the relative syntactic dependency distance between words is calculated, so that words can focus on the syntactic features of words closer to them. Subsequently, the dual affine attention mechanism combined with GCN is used to enhance semantic and syntactic features. The GCN and dual affine attention mechanism can effectively utilize the structural information of syntactic dependency trees and integrate them into the model. Finally, the global features and language features are fused to ensure that key information in the global context is not ignored, thereby improving the model's robustness. The experimental results show that compared with GCN-EGTS-BERT model, the LFE model improves the F1 values by 3.52, 5.32, 1.97, and 2.63 percentage points on four datasets: Res14, Lap14, Res15, and Res16, respectively, demonstrating its feasibility and effectiveness.

Key words: aspect sentiment triplet extraction, linguistic features, keyword lexicality, relative syntactic dependency distance, Graph Convolutional Networks (GCN)