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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.18908 |
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| _version_ | 1866909441100611584 |
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| author | Li, JiaCheng |
| author_facet | Li, JiaCheng |
| contents | With the explosive growth of Chinese text data and advancements in natural language processing technologies, Chinese text classification has become one of the key techniques in fields such as information retrieval and sentiment analysis, attracting increasing attention. This paper conducts a comparative study on three deep learning models:TextCNN, TextRNN, and FastText.specifically for Chinese text classification tasks. By conducting experiments on the THUCNews dataset, the performance of these models is evaluated, and their applicability in different scenarios is discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18908 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks Li, JiaCheng Computation and Language With the explosive growth of Chinese text data and advancements in natural language processing technologies, Chinese text classification has become one of the key techniques in fields such as information retrieval and sentiment analysis, attracting increasing attention. This paper conducts a comparative study on three deep learning models:TextCNN, TextRNN, and FastText.specifically for Chinese text classification tasks. By conducting experiments on the THUCNews dataset, the performance of these models is evaluated, and their applicability in different scenarios is discussed. |
| title | Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.18908 |