COLING 2020main283 citations
An Analysis of Simple Data Augmentation for Named Entity Recognition
Abstract
Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by these efforts, we design and compare data augmentation for named entity recognition, which is usually modeled as a token-level sequence labeling problem. Through experiments on two data sets from the biomedical and materials science domains (i2b2-2010 and MaSciP), we show that simple augmentation can boost performance for both recurrent and transformer-based models, especially for small training sets.
BibTeX
@inproceedings{dai-adel-2020-analysis,
title = "An Analysis of Simple Data Augmentation for Named Entity Recognition",
author = "Dai, Xiang and
Adel, Heike",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.343/",
doi = "10.18653/v1/2020.coling-main.343",
pages = "3861--3867"
}