A Global-Local Attention Mechanism for Relation Classification
This work addresses relation extraction for natural language processing by improving classification accuracy, though it is incremental as it builds on existing attention mechanisms.
The paper tackled relation classification by introducing a global-local attention mechanism to address the oversight of local context in previous methods, achieving superior performance on the SemEval-2010 Task 8 dataset compared to prior attention-based approaches.
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.