Feng, JHMohaghegh, M2021-11-302021-11-302021-10-252021-10-25In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KMIS, ISBN 978-989-758-533-3; ISSN 2184-3228, pages 194-197. DOI: 10.5220/0010688500003064978-989-758-533-32184-3228https://hdl.handle.net/10292/14756Data augmentation techniques have been increasingly explored in natural language processing to create more textual data for training. However, the performance gain of existing techniques is often marginal. This paper explores the performance of combining two EDA (Easy Data Augmentation) methods, random swap and random delete for the performance in text classification. The classification tasks were conducted using CNN as a text classifier model on a portion of the SST-2: Stanford Sentiment Treebank dataset. The results show that the performance gain of this hybrid model performs worse than the benchmark accuracy. The research can be continued with a different combination of methods and experimented on larger datasets.Copyright (c) 2021 by SCITEPRESS – Science and Technology Publications. Creative Commons License. CC BY-NC-ND 4.0Data Augmentation; Hybrid Models; Machine Learning; Natural Language ProcessingConference ContributionOpenAccess10.5220/0010688500003064