Abstract
Text classification is an essential part of natural language processing. With the development of deep learning technology, using deep learning for text classification has become mainstream. However, deep learning methods often require a considerable quantity of data, and collecting and processing data sets is cumbersome and expensive. This paper presents a data enhancement method simulating the generation of acronyms, quickly expanding the text classification datasets. We show the performance of our method on three classical data sets and compare it with two classical text data enhancement methods. The results show that this method can effectively improve the effect of text classification. Meanwhile, by comparing the similarity with the original sentence, it is found that our method has very little change in sentence semantics and is more robust than the baseline method.
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Ou, L., Chen, H., Luo, X., Li, X., Chen, S. (2022). ADA: An Acronym-Based Data Augmentation Method for Low-Resource Text Classification. In: Proceedings of 2022 10th China Conference on Command and Control. C2 2022. Lecture Notes in Electrical Engineering, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-19-6052-9_35
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DOI: https://doi.org/10.1007/978-981-19-6052-9_35
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