@inproceedings{fang-etal-2021-tebner,
title = "{TEBNER}: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network",
author = "Fang, Zheng and
Cao, Yanan and
Li, Tai and
Jia, Ruipeng and
Fang, Fang and
Shang, Yanmin and
Lu, Yuhai",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.18",
doi = "10.18653/v1/2021.emnlp-main.18",
pages = "198--207",
abstract = "To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities. Although the human effort is reduced, the generated incomplete and noisy annotations pose new challenges for learning effective neural models. In this paper, we propose a novel dictionary extension method which extracts new entities through the type expanded model. Moreover, we design a multi-granularity boundary-aware network which detects entity boundaries from both local and global perspectives. We conduct experiments on different types of datasets, the results show that our model outperforms previous state-of-the-art distantly supervised systems and even surpasses the supervised models.",
}
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<abstract>To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities. Although the human effort is reduced, the generated incomplete and noisy annotations pose new challenges for learning effective neural models. In this paper, we propose a novel dictionary extension method which extracts new entities through the type expanded model. Moreover, we design a multi-granularity boundary-aware network which detects entity boundaries from both local and global perspectives. We conduct experiments on different types of datasets, the results show that our model outperforms previous state-of-the-art distantly supervised systems and even surpasses the supervised models.</abstract>
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%0 Conference Proceedings
%T TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network
%A Fang, Zheng
%A Cao, Yanan
%A Li, Tai
%A Jia, Ruipeng
%A Fang, Fang
%A Shang, Yanmin
%A Lu, Yuhai
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F fang-etal-2021-tebner
%X To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities. Although the human effort is reduced, the generated incomplete and noisy annotations pose new challenges for learning effective neural models. In this paper, we propose a novel dictionary extension method which extracts new entities through the type expanded model. Moreover, we design a multi-granularity boundary-aware network which detects entity boundaries from both local and global perspectives. We conduct experiments on different types of datasets, the results show that our model outperforms previous state-of-the-art distantly supervised systems and even surpasses the supervised models.
%R 10.18653/v1/2021.emnlp-main.18
%U https://aclanthology.org/2021.emnlp-main.18
%U https://doi.org/10.18653/v1/2021.emnlp-main.18
%P 198-207
Markdown (Informal)
[TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network](https://aclanthology.org/2021.emnlp-main.18) (Fang et al., EMNLP 2021)
ACL