Li, Q, Peng, H, Li, J et al. (5 more authors) (2022) A Survey on Text Classification: From Traditional to Deep Learning. ACM Transactions on Intelligent Systems and Technology, 13 (2). 31. ISSN 2157-6904
Abstract
Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021, focusing on models from traditional models to deep learning. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification. We then discuss each of these categories in detail, dealing with both the technical developments and benchmark datasets that support tests of predictions. A comprehensive comparison between different techniques, as well as identifying the pros and cons of various evaluation metrics are also provided in this survey. Finally, we conclude by summarizing key implications, future research directions, and the challenges facing the research area.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Keywords: | challenges; Deep learning; evaluation metrics; text classification; traditional models |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 May 2022 12:17 |
Last Modified: | 12 May 2022 12:17 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3495162 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186633 |