Relation classification, a crucial component of relation extraction, involves identifying connections between two entities. Previous studies have predominantly focused on integrating the attention mechanism into relation classification at a global scale, overlooking the importance of the local context. To address this gap, this paper introduces a novel global-local attention mechanism for relation classification, which enhances global attention with a localized focus. Additionally, we propose innovative hard and soft localization mechanisms to identify potential keywords for local attention. By incorporating both hard and soft localization strategies, our approach offers a more nuanced and comprehensive understanding of the contextual cues that contribute to effective relation classification. Our experimental results on the SemEval-2010 Task 8 dataset highlight the superior performance of our method compared to previous attention-based approaches in relation classification.

翻译：关系分类作为关系抽取的关键组成部分，涉及识别两个实体之间的关联。先前的研究主要集中于将注意力机制在全局层面整合到关系分类中，忽视了局部上下文的重要性。为弥补这一不足，本文提出了一种新颖的面向关系分类的全局-局部注意力机制，该机制通过局部聚焦增强了全局注意力。此外，我们提出了创新的硬定位与软定位机制，以识别适用于局部注意力的潜在关键词。通过结合硬定位与软定位策略，我们的方法能够更细致、更全面地理解有助于实现有效关系分类的上下文线索。我们在SemEval-2010 Task 8数据集上的实验结果凸显了本方法相较于以往基于注意力的关系分类方法的优越性能。