对比三种模型在中文文本分类中的表现
Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks
- 用TextCNN、TextRNN、FastText在THUCNews上做对比实验
- FastText准确率最高，达89.2%，TextRNN最低
- 适合想快速部署的开发者参考
随着中文文本数据的爆炸式增长和自然语言处理技术的进步，中文文本分类已成为信息检索、情感分析等领域的关键技术，受到越来越多关注。本文针对中文文本分类任务，对TextCNN、TextRNN和FastText三种深度学习模型进行了比较研究。通过在THUCNews数据集上的实验，评估了这些模型的性能，并讨论了它们在不同场景下的适用性。
原文摘要 · Abstract (English)
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.
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