A Global-Local Attention Mechanism for Relation Classification

TL;DR

This paper proposes a novel global-local attention mechanism for relation classification that combines global context with local focus, improving performance by identifying key local cues through innovative localization strategies.

Contribution

It introduces a new global-local attention mechanism with hard and soft localization methods, addressing the gap in local context modeling in relation classification.

Findings

Outperforms previous attention-based methods on SemEval-2010 Task 8

Demonstrates the effectiveness of combined global and local attention

Shows improved accuracy in relation classification tasks

Abstract

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.