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Improving Fine-grained Entity Typing with Entity Linking

2019-09-26IJCNLP 2019Code Available0· sign in to hype

Hongliang Dai, Donghong Du, Xin Li, Yangqiu Song

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Abstract

Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. In this paper, we use entity linking to help with the fine-grained entity type classification process. We propose a deep neural model that makes predictions based on both the context and the information obtained from entity linking results. Experimental results on two commonly used datasets demonstrates the effectiveness of our approach. On both datasets, it achieves more than 5\% absolute strict accuracy improvement over the state of the art.

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