An Analysis of Simple Data Augmentation for Named Entity Recognition

By Xiang Dai, Heike Adel. 2020

234 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, background): “Dai and Adel (2020) modify DA techniques proposed for sentence-level tasks for named entity recognition (NER), including label-wise token and synonym replacement, and show improved performance using both recurrent and transformer models.”
Syntax-Aware Data Augmentation for Neural Machine Translation (2020, methodology): “Data augmentation is a training enhancement paradigm that has been broadly used in CV [1], [33], [34], [35] and NLP [2], [3], [4], [5], [6], [7], [8], [9], [36], [37].”
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2020, background): “2019), entities of the same type (Raiman and Miller, 2017; Dai and Adel, 2020) or words that share the same morphology (Gulordava et al.”
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (2021, methodology): “This transformation is inspired by Dai and Adel (2020).”
Data Augmentation Approaches in Natural Language Processing: A Survey (2021, background): “Substitution [42], [10], [12], [7], [102] [23], [71], [21] [42], [11], [43], [114]”
Intriguing Properties of Compression on Multilingual Models (2022, methodology): “The technique we use to study robustness expands on studies that perturb training (Yaseen and Langer, 2021; Dai and Adel, 2020) or evaluation data (Dhole et al., 2021) in NER by introducing perturbations speciﬁc to languages, language families, and scripts.”
Substructure Substitution: Structured Data Augmentation for NLP (2021, methodology): “Other token-level manipulation methods introduce noise, such as random token shuffling and deletion (Wang et al., 2018; Wei and Zou, 2019; Dai and Adel, 2020).”