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.

本文介绍了一种新颖的全局 - 局部注意机制用于关系分类，通过将全局注意力与局部关注度相结合来提升全局关注度。此外，我们提出了创新的硬与软定位机制，以识别局部注意的潜在关键字。通过同时融入硬和软定位策略，我们的方法对有效关系分类的语境线索提供了更加细致全面的理解。我们在 SemEval-2010 任务 8 数据集上的实验结果显示了我们的方法相对于前期关注机制的优越性能。