Abstract:

Aspect Sentiment Triplet Extraction （ASTE） identifies sentiment entities， attributes， and polarities， enabling precise analysis of sentiment associations in user opinions. This supports product and service optimization， public opinion monitoring， and consumer decision-making. To address limitations of traditional methods in modeling long-range semantic dependencies and insufficient target-domain data， a cross-domain data augmentation graph convolutional network model was proposed for ASTE. During cross-domain data augmentation stage， pseudo-label generation technology based on Maximum Mean Discrepancy （MMD） was introduced to label unlabeled target-domain data initially to mitigate domain shift， a domain-adaptive language model was trained to capture target-domain-specific semantics， and autoregressive data generation was performed by this domain-adaptive language model， so as to further expand the target-domain labeled data volume and diversity. During sentiment triplet extraction stage， the data after first-stage augmentation were used to fine-tune the graph convolutional network-based language model in the pseudo-label generation process， then the fine-tuned model was employed for sentiment triplet extraction. Experimental results on the ASTE-DATA-V2 dataset show that the proposed method outperforms baseline methods such as BGCA （Bidirectional Generative Cross-domain ABSA）， FOAL （Fine-grained cOntrAstive Learning）， and HiPM-hard （Hybrid Prompts Mixture）. Compared with the baseline BGCA， the proposed method has the average F1 score increased by 0.81 percentage points， improving the accuracy and stability of cross-domain sentiment triplet extraction.

Key words: Aspect Sentiment Triplet Extraction (ASTE), cross-domain, pseudo-label, data augmentation, Graph Convolutional Network (GCN)

摘要：

方面级情感三元组提取（ASTE）通过识别情感主体、属性及其极性，能精准解析用户观点中的情感关联，为产品服务优化、舆情监控和消费决策提供有力支持。针对传统方法难以建模长距离语义依赖和目标领域数据不足等问题，提出一种跨领域数据增强的图卷积网络模型用于ASTE。在跨领域数据增强阶段，通过引入基于最大均值差异（MMD）的伪标注生成技术对目标域的未标注数据进行初步标注以缓解领域漂移，训练领域自适应语言模型以捕捉目标领域特有的语义，并基于该领域的自适应语言模型生成自回归数据，进一步增加目标域的标注数据量和多样性；在情感三元组抽取阶段，利用第一阶段增强后的数据对伪标注生成过程中产生的基于图卷积网络的语言模型进行微调，然后利用微调后的图卷积网络语言模型进行情感三元组抽取。在ASTE-DATA-V2数据集上的实验结果表明，该方法优于BGCA （Bidirectional Generative Cross-domain ABSA）、FOAL （Fine-grained cOntrAstive Learning）和HiPM-hard （Hybrid Prompts Mixture）等基准方法，相较于基线BGCA在平均F1分数上提高了0.81个百分点，从而提高了跨领域情感三元组提取的准确性与稳定性。

关键词: 方面级情感三元组抽取, 跨领域, 伪标签, 数据增强, 图卷积网络