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arXiv · 1910.02724

Improving Relation Extraction with Knowledge-attention

Pengfei Li·Kezhi Mao·Xuefeng Yang·Qi Li
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Abstract

While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven. We propose a novel knowledge-attention encoder which incorporates prior knowledge from external lexical resources into deep neural networks for relation extraction task. Furthermore, we present three effective ways of integrating knowledge-attention with self-attention to maximize the utilization of both knowledge and data. The proposed relation extraction system is end-to-end and fully attention-based. Experiment results show that the proposed knowledge-attention mechanism has complementary strengths with self-attention, and our integrated models outperform existing CNN, RNN, and self-attention based models. State-of-the-art performance is achieved on TACRED, a complex and large-scale relation extraction dataset.

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Pengfei Li, Kezhi Mao, Xuefeng Yang, Qi Li. 2020-03-04. Improving Relation Extraction with Knowledge-attention. https://doi.org/10.18653/v1%2Fd19-1022

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