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Text feature extraction based on deep learning: a review
Open accessAbstract
Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
Machine learning in automated text categorization
Sebastiani, F. (2002) · ACM Computing Surveys · doi:10.1145/505282.505283 · Cited by 7,927
Research works printed but not linked: 9
- D Mladenic, Machine learning on non-homogeneous, distributed text data, PhD Thesis. Web. (1998)
- S Niharika, VS Latha, DR Lavanya, A survey on text categorization. Int. J. Compt. Trends Technol. 3(1), 39-45 (2006)
- Y Luo, S Zhao, et al, Text keyword extraction method based on word frequency statistics. J. Compt. Appl. 36(3), 718–725 (2016)
- AK Uysal, S Gunal, A novel probabilistic feature selection method for text classification[J]. Knowledge-Based Syst. 36(6), 226-235 (2012. · DOI
- KK Bharti, PK Singh, Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering[J]. Expert Syst. Appl. 42(6), 3105-3114 (2015) · DOI
- S Luo, The feature extraction of text category and text fuzzy matching based on concept. Computer Engineering and Applications 38(16), 97–98 (2002)
- S Qin, Z Lu, Sparse automatic encoder application in text categorization research. Sciencetechnology and engineering 13(31), 9422–9426 (2013)
- YN Chen, CC Han, CT Wang, et al, The Application of a Convolution Neural Network on Face and License Plate Detection[C]// International Conference on Pattern Recognition. (IEEE, 2006), pp. 552-555
- S Hochreiter, Untersuchungen zu dynamischen neuronalen Netzen[C]// Master's Thesis. (Institut Fur Informatik, Technische Universitat, Munchen, 1991), p. 1-70
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Explaining a series of models by propagating Shapley values
Chen, H., Lundberg, S., & Lee, S. (2022) · Nature Communications · doi:10.1038/s41467-022-31384-3 · Cited by 303
Medical deep learning—A systematic meta-review
Egger, J., Gsaxner, C., Pepe, A., Pomykala, K. L., Jonske, F., Kurz, M., Li, J., & Kleesiek, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106874 · Cited by 289
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Tembhurne, J. V., Hebbar, N., Patil, H. Y., & Diwan, T. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14697-3 · Cited by 197
Metadata
- Authors
- Liang, H., Sun, X., Sun, Y., & Gao, Y.
- Affiliations
- China University of Petroleum, East China (CN)
- Corresponding author
- Sun, Yunlei
- Venue
- EURASIP Journal on Wireless Communications and Networking
- Published
- 2017-12
- Volume
- 2017
- Issue
- 1
- Pages
- 211-211
- ISSN
- 1687-1472 (ISSN-L)
- 1687-1499
- PMC
- PMC5732309
- OpenAlex
- W2772515592
- Cited by
- 373
- References
- 130
- Citations by year
- 2026: 21 · 2025: 47 · 2024: 57 · 2023: 56 · 2022: 78 · 2021: 43 · 2020: 41 · 2019: 19 · 2018: 11
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Update notices
- correction (2018-02-16)
- Open access
- gold
- doi.org
- Locations
- EURASIP Journal on Wireless Communications and Networking (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (Grant No.18CX02019A)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- text feature extraction, deep learning, automatic feature learning, feature representation, text mining, information retrieval, handcrafted features, big data
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