Education, Science, Technology, Innovation and Life
Open Access
Sign In

A Comprehensive Review of Text Classification Algorithms

Download as PDF

DOI: 10.23977/jeis.2024.090205 | Downloads: 17 | Views: 210

Author(s)

Yachang Song 1, Xinyu Liu 1, Ze Zhou 1

Affiliation(s)

1 College of Electronic Information, Xijing University, Xian, China

Corresponding Author

Yachang Song

ABSTRACT

This paper reviews the development of text classification algorithms, from rule-based and traditional machine learning methods to the evolution of deep learning and pre-trained models. As a crucial aspect of natural language processing, text classification is essential for various applications, such as information retrieval and sentiment analysis. The application of deep learning models like CNNs, RNNs, and pre-trained models such as BERT in text classification is highlighted, showcasing their advantages in processing large corpora. The challenges and future research directions in text classification are also discussed, offering guidance to researchers and practitioners in the field.

KEYWORDS

Text Classification, Deep Learning, Pre-trained Models, Natural Language Processing, CNN, RNN, BERT

CITE THIS PAPER

Yachang Song, Xinyu Liu, Ze Zhou, A Comprehensive Review of Text Classification Algorithms. Journal of Electronics and Information Science (2024) Vol. 9: 34-42. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090205.

REFERENCES

[1] Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys (CSUR), 2002, 34(1):1-47.
[2] Maron M E. Automatic indexing: an experimental inquiry. Journal of the ACM (JACM), 1961, 8(3):404-417.
[3] Joachims T. Text categorization with support vector machines: Learning with many relevant features//Lecture Notes in Computer Science: volume 1398 Machine Learning: ECML-98, 10th European Conference on Machine Learning, Chemnitz, Germany, 1998: 137- 142.
[4] Unanue I J, Haffari G, Piccardi M. T3l: Translate-and-test transfer learning for cross-lingual text classification. arXiv preprint arXiv:2306.04996, 2023.
[5] Joulin A, Grave E, Bojanowski P, et al. Bag of tricks for efficient text classification//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017, Valencia, Spain, Volume 2: Short Papers, 2017: 427- 431.
[6] Mikolov T, Karafiát M, Burget L, et al. Recurrent neural network based language model//INTERSPEECH 2010, 11th Annual Conference of the International Speech Communication Association, Makuhari, Japan, 2010: 1045-1048.
[7] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8):1735-1780.
[8] SCARSELLI F, GORI M, TSOI A C, et al. The graph neural network model. IEEE Transactions on Neural Networks, 2008, 20(1):61-80.
[9] Zhuang F, Qi Z, Duan K, et al. A comprehensive survey on transfer learning. Proceedings of the IEEE, 2020, 109(1):43-76.
[10] Howard J, Ruder S. Universal language model fine-tuning for text classification//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, Volume 1: Long Papers, 2018: 328-339.
[11] Wu T, Caccia M, Li Z, et al. Pretrained language model in continual learning: A comparative study//The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, 2022.
[12] Devlin J, Chang M, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, USA, Volume 1 (Long and Short Papers), 2019: 4171-4186.
[13] Ikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 2013, 26.

Downloads: 8294
Visits: 280050

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.