A Global-Local Attention Mechanism for Relation Classification
<|reference_start|>A Global-Local Attention Mechanism for Relation Classification: 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.<|reference_end|>
@article{sun2024a,
    title={A Global-Local Attention Mechanism for Relation Classification},
    author={Yiping Sun},
    journal={arXiv preprint arXiv:2407.01424},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01424},
    primaryClass={cs.CL cs.IR}
}