Improving Fine-grained Entity Typing with Entity Linking

Hongliang Dai, Donghong Du, Xin Li, Yangqiu Song

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.
Anthology ID:
D19-1643
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6210–6215
Language:
URL:
https://aclanthology.org/D19-1643
DOI:
10.18653/v1/D19-1643
Bibkey:
Cite (ACL):
Hongliang Dai, Donghong Du, Xin Li, and Yangqiu Song. 2019. Improving Fine-grained Entity Typing with Entity Linking. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6210–6215, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Improving Fine-grained Entity Typing with Entity Linking (Dai et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/D19-1643.pdf
Code
 HKUST-KnowComp/IFETEL
Data
FIGER
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