TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network
Zheng Fang, Yanan Cao, Tai Li, Ruipeng Jia, Fang Fang, Yanmin Shang, Yuhai Lu
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.
BibTeX
@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"
}