Hybrid Model of Data Augmentation Methods for Text Classification Task

Date
2021-10-25
Authors
Feng, JH
Mohaghegh, M
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
SCITEPRESS
Abstract

Data 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.

Description
Keywords
Data Augmentation; Hybrid Models; Machine Learning; Natural Language Processing
Source
In 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: 10.5220/0010688500003064
Rights statement
Copyright (c) 2021 by SCITEPRESS – Science and Technology Publications. Creative Commons License. CC BY-NC-ND 4.0