The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training and testing data are from the same domain. However, these methods show a drastic decrease in performance when applied to cross-domain settings where the domain of the testing data differs from that of the training data. To address this lack of extensibility and robustness, we propose a novel approach for automatically constructing domain-specific knowledge graphs that contain information relevant to the identification of aspect terms. We introduce a methodology for injecting information from these knowledge graphs into Transformer models, including two alternative mechanisms for knowledge insertion: via query enrichment and via manipulation of attention patterns. We demonstrate state-of-the-art performance on benchmark datasets for cross-domain aspect term extraction using our approach and investigate how the amount of external knowledge available to the Transformer impacts model performance.

翻译：对文本进行细微的情绪分析的关键步骤是提取侧面术语。当培训和测试数据来自同一领域时,这项任务的现有方法取得了令人印象深刻的成果。然而,这些方法显示,在将测试数据领域与培训数据领域不同的跨域环境应用到跨域设置时,性能急剧下降。为了解决这种缺乏存在性和可靠性的问题,我们建议了一种新颖的办法,自动构建包含与确定侧面术语有关的信息的特定域知识图表。我们引入了将这些知识图表的信息注入变异模型的方法,包括两种知识插入替代机制:通过查询浓缩和操纵注意力模式。我们用我们的方法展示了跨域提取基准数据集的最新性业绩,并调查了变异器模型绩效所获得的外部知识的数量。