A Study of Various Text Augmentation Techniques for Relation Classification in Free Text

By Praveen Kumar Badimala Giridhara, Chinmaya Mishra, R. Venkataramana, Syed Saqib Bukhari, Andreas R. Dengel. 2019

40 citations recorded by the source corpus.

Data augmentation techniques have been widely used in visual recognition tasks as it is easy to generate new data by simple and straight forward image transformations. However, when it comes to text data augmentations, it is difficult to find appropriate transformation techniques which also preserve the contextual and grammatical structure of language texts. In this paper, we explore various text data augmentation techniques in text space and word embedding space. We study the effect of various augmented datasets on the efficiency of different deep learning models for relation classification in text.

Subfields: Artificial Intelligence, Natural Language Processing, Information Extraction

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Citations in the corpus

Learning Data Manipulation for Augmentation and Weighting (2019, background): “The first example learns augmentation for text data, a domain that has been less studied in the literature compared to vision and speech [25, 12].”
Multilingual Sentiment Analysis and Toxicity Detection for Text Messages in Russian (2021, methodology): “When augmenting textual data, it is important to preserve the meaning of the text, often by replacing words with syn-onyms using various dictionaries, for example, WordNet [22] or using pre-trained language models, such as BERT, GPT2, Word2Vec, Glove [23]–[25].”