The DOI matches its Crossref record; 162 of the 162 references it cites were found in a registry or an index.
Deep Learning--based Text Classification: A Comprehensive Review
Abstract
Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions.
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Metadata
- Authors
- Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J.
- Venue
- ACM Computing Surveys
- Published
- 2021-04-17
- Volume
- 54
- Issue
- 3
- Pages
- 1-40
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3156333129
- Cited by
- 1,538
- References
- 162
- FWCI
- 90.25
- Type
- journal article
- Language
- en
- Open access
- closed
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- Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2022). Deep Learning--based Text Classification: A Comprehensive Review. ACM Computing Surveys. https://doi.org/10.1145/3439726
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