基于文本分析和深度学习的文学作品的作者识别
1College of Literature, Chongqing Normal University, Chongqing, 401331, China.
Heliyon
|February 8, 2024
概括
这项研究介绍了一种深度学习模型,用于识别中国文学作者. 该模型有效地提取文本特征并对作者进行分类,在准确性方面表现优于基准模型.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 中国文本分析提出了超越统计方法的独特挑战,需要语义理解.
- 有效的语言风格特征建模对于在不同类型中准确的文本分类至关重要.
研究的目的:
- 开发一个自动的文本特征提取和分类模型,用于使用深度学习识别中国文学作者.
- 为了提高识别作者基于他们的文学作品的准确性.
主要方法:
- 一个包含Word2Vec的深度学习模型,用于单词矢量生成.
- 使用卷积神经网络 (CNN) 和注意力机制改进的文本功能提取器.
- 使用长短期内存 (LSTM) 和Softmax进行分类,具有完全连接和批量规范化层.
主要成果:
- 拟议的模型有效地提取文本特征,并在作者识别方面实现高精度.
- 在包括散文和小说在内的多种数据集上进行评估,该模型与基准方法相比表现优越.
- 分类准确度明显超过现有模型.
结论:
- 深度学习为复杂的中文文本分析提供了强大的方法,特别是作者归因.
- 开发的模型为识别中国文学作家提供了有效的解决方案.
- 这些发现表明,在自动识别文学作者方面取得了重大进展.
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