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An Analysis of Simple Data Augmentation for Named Entity Recognition

2020-10-22 13:21:03
Xiang Dai, Heike Adel
arXiv_CL
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2010.11683

PDF

https://arxiv.org/pdf/2010.11683.pdf

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