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An Analysis of Simple Data Augmentation for Named Entity Recognition
[article]
2020
arXiv
pre-print
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
arXiv:2010.11683v1
fatcat:zsv2uqqmafej3jq7aip53cuqkm