@inproceedings{dai-etal-2019-improving,
title = "Improving Fine-grained Entity Typing with Entity Linking",
author = "Dai, Hongliang and
Du, Donghong and
Li, Xin and
Song, Yangqiu",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1643",
doi = "10.18653/v1/D19-1643",
pages = "6210--6215",
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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<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.</abstract>
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%0 Conference Proceedings
%T Improving Fine-grained Entity Typing with Entity Linking
%A Dai, Hongliang
%A Du, Donghong
%A Li, Xin
%A Song, Yangqiu
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F dai-etal-2019-improving
%X 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.
%R 10.18653/v1/D19-1643
%U https://aclanthology.org/D19-1643
%U https://doi.org/10.18653/v1/D19-1643
%P 6210-6215
Markdown (Informal)
[Improving Fine-grained Entity Typing with Entity Linking](https://aclanthology.org/D19-1643) (Dai et al., EMNLP-IJCNLP 2019)
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.