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- Kamran Kowsari
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
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- Kiana Jafari Meimandi
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
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- Mojtaba Heidarysafa
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
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- Sanjana Mendu
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
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- Laura Barnes
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
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- Donald Brown
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA
説明
<jats:p>In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed.</jats:p>
収録刊行物
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- Information
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Information 10 (4), 150-, 2019-04-23
MDPI AG
