Data augmentation in natural language processing: a novel text generation approach for long and short text classifiers

  title={Data augmentation in natural language processing: a novel text generation approach for long and short text classifiers},
  author={Markus Bayer and Marc-Andr{\'e} Kaufhold and Bj{\"o}rn Buchhold and Marcel Keller and J{\"o}rg Dallmeyer and Christian Reuter},
  journal={International Journal of Machine Learning and Cybernetics},
  pages={1 - 16}
In many cases of machine learning, research suggests that the development of training data might have a higher relevance than the choice and modelling of classifiers themselves. Thus, data augmentation methods have been developed to improve classifiers by artificially created training data. In NLP, there is the challenge of establishing universal rules for text transformations which provide new linguistic patterns. In this paper, we present and evaluate a text generation method suitable to… 

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