An Analysis of Simple Data Augmentation for Named Entity Recognition

• URL: http://arxiv.org/abs/2010.11683v1
• Date: Thu, 22 Oct 2020 13:21:03 GMT
• Title: An Analysis of Simple Data Augmentation for Named Entity Recognition
• Authors: Xiang Dai and Heike Adel
• Abstract summary: We design and compare data augmentation for named entity recognition. We show that simple augmentation can boost performance for both recurrent and transformer-based models.
• Score: 21.013836715832564
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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