识别作家风格所需的数据量在世界不同语言中是一致的
Boris Ryabko1,2, Nadezhda Savina2, Yeshewas Getachew Lulu2
1Federal Research Center for Information and Computational Technologies, 6300090 Novosibirsk, Russia.
Entropy (Basel, Switzerland)
|October 28, 2025
概括
这项研究使用数据压缩来识别俄语,阿姆哈拉语,中文和英语的作者写作风格. 识别作者所需的数据量在这些不同的语言中都是一致的.
科学领域:
- 计算语言学 计算语言学
- 文学研究 文学研究
- 信息理论 信息理论
背景情况:
- 在数字取证和文学分析中,识别作者风格至关重要.
- 现有的方法可能取决于语言,限制了跨语言的应用.
- 信息理论方法为文本分析提供了新的视角.
研究的目的:
- 研究一种信息理论方法的跨语言适用性,用于识别作者风格.
- 为了确定作者识别所需的数据是否在不同语言家族中不变.
- 探索数据压缩技术在计算语言学中的潜力.
主要方法:
- 应用Ryabko-Savina (RS) 方法,一种信息理论方法.
- 使用数据压缩原则对文字数据进行定量分析.
- 从四种不同的语言分析小说文本:俄语,阿姆哈拉语,中文和英语.
主要成果:
- RS方法成功地识别了研究语言中的个别作者风格.
- 发现,识别作者风格所需的数据量在不同语言家族的语言中是不变的.
- 这种不变性表明,作者风格的普遍特征可以通过数据压缩来衡量.
结论:
- 信息理论的RS方法对于跨语言的作者风格识别是有效的.
- 作者的风格表现出数据不变性,这是计算语言学的一个重要发现.
- 这项研究将计算机科学,语言学和文学研究联系在一起,对文本分析有实际意义.
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