![]() So, X’ could be syntactically a little different compared to X, but semantically it should mean the same thing. So, as you can see since Y is still preserved, which means the transformation that we want to apply, say, T, has to be semantically invariant which means it doesn’t change the meaning of the original sentence. So, we can imagine it to be like X is a movie review and Y is the sentiment associated with that review.Īs a part of data augmentation, we transform this X and create X’ out of it, while still preserving the label Y. So what it means is, let’s say you have data (X, Y), where X is a sentence and Y is its corresponding label. This simply means we want to generate more data and more examples from our current dataset. Based on my findings, I’ll present an overview of existing approaches for text data augmentation in this article and introduce the python library ‘NLPAug’.ĭata Augmentation describes applying transformations to our original labeled examples to construct new data for the training set I looked through the current literature to see whether there had been any attempts to build augmentation approaches for NLP. Because of the semantically invariant transformation, augmentation has become an important tool in Computer Vision research. ![]() ![]() Simple manipulations on images, such as rotating them a few degrees or turning them to grayscale, have little effect on their semantics. In contrast to Computer Vision, where image data augmentation is common, text data augmentation in NLP is uncommon. ![]()
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