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A Survey on Data Augmentation for Text Classification
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DOI：10.1145/3544558.png)
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摘要
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Data augmentation, the artificial creation of training data formachine learning by transformations, is awidely studied research field across machine learning disciplines. While it is useful for increasing a model's generalization capabilities, it can also address many other challenges and problems, from overcoming a limited amount of training data to regularizing the objective, to limiting the amount of data used to protect privacy. Based on a precise description of the goals and applications of data augmentation and a taxonomy for existing works, this survey is concerned with data augmentation methods for textual classification and aims at providing a concise and comprehensive overview for researchers and practitioners. Derived from the taxonomy, we divide more than 100 methods into 12 different groupings and give state-of-the-art references expounding which methods are highly promising by relating them to each other. Finally, research perspectives that may constitute a building block for future work are provided.
Keyword:
Data augmentation
low data regimes
small data analytics
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28
论文数:
2.4K
被引数:
3.5W
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