Data augmentation technology for named entity recognition

doi:10.3969/j.issn.1000-5641.2021.05.002

Abstract

Abstract:

A named entity recognition task is as a task that involves extracting instances of a named entity from continuous natural language text. Named entity recognition plays an important role in information extraction and is closely related to other information extraction tasks. In recent years, deep learning methods have been widely used in named entity recognition tasks; the methods, in fact, have achieved a good performance level. The most common named entity recognition models use sequence tagging, which relies on the availability of a high quality annotation corpus. However, the annotation cost of sequence data is high; this leads to the use of small training sets and, in turn, seriously limits the final performance of named entity recognition models. To enlarge the size of training sets for named entity recognition without increasing the associated labor cost, this paper proposes a data augmentation method for named entity recognition based on EDA, distant supervision, and bootstrap. Using experiments on the FIND-2019 dataset, this paper illustrates that the proposed data augmentation techniques and combinations thereof can significantly improve the overall performance of named entity recognition models.

Key words: named entity recognition, data augmentation, EDA, distant supervision, Bootstrap

CLC Number:

TP311

Xiaoqin MA, Xiaohe GUO, Yufeng XUE, Lin YANG, Yuanzhe CHEN. Data augmentation technology for named entity recognition[J]. Journal of East China Normal University(Natural Science), 2021, 2021(5): 14-23.

Figures/Tables 6

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不同EDA方法	0	5%	10%	15%	20%	25%	30%	35%	40%	45%	50%
原始EDA	55.43	55.56	55.40	55.43	55.21	55.07	54.84	54.76	54.39	54.33	54.25
改进EDA(简化版)	55.43	55.78	55.92	55.95	55.96	55.93	55.81	55.85	55.72	55.68	55.65
改进EDA(完整版)	55.43	55.97	56.35	56.44	56.12	56.15	55.85	56.02	55.97	55.83	55.76

远程监督场景	0	50%	100%	150%	200%	250%	300%	350%	400%	450%	500%
冷启动	0	32.54	39.67	41.75	43.22	43.48	43.57	43.60	43.62	43.65	43.63
数据增强	55.43	55.72	55.84	55.50	54.61	54.72	53.34	52.88	52.09	51.43	51.15

组合	best_EDA	best_DIS	best_BS	F₁
无数据增强				55.43
仅EDA	10%			56.35
仅远程监督		100%		55.84
仅Bootstrap			4	56.55
EDA + 远程监督	10%	50%		56.62
EDA + Bootstrap	10%		3	57.16
远程监督 + Bootstrap		50%	3	56.89
EDA + 远程监督 + Bootstrap	5%	50%	2	57.37

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