日本名字中的声音象征:机器学习对性别分类的方法
Chun Hau Ngai1, Alexander J Kilpatrick2, Aleksandra Ćwiek3
1East Asian Languages and Cultures Department, Indiana University, Bloomington, Indiana, United States of America.
PloS one
|March 11, 2024
概括
日本名字透露性别通过声音象征. 机器学习模型,特别是XGBoost,准确地分类性别,识别独特的声音含义关联,如女性的 / m / 和 / k / 和男性的 / i / .
科学领域:
- 计算语言学 计算语言学
- 心理语言学 心理语言学
- 声音象征研究研究 声音象征研究
背景情况:
- 探讨声音象征主义的语言现象,其中语音带有固有的含义.
- 检查印欧语言中普遍存在的声音含义映射是否适用于日本名称.
- 解决了在声音象征主义中理解文化和语言特异性的需要.
研究的目的:
- 通过机器学习探索如何在日本名字中表达性别的语音.
- 为了比较随机森林和XGBoost算法在名称的性别分类中的有效性.
- 在日本名字中识别与男性和女性性别相关的特定音符.
主要方法:
- 训练随机森林和XGBoost机器学习模型在日本名字音符和相关的性别.
- 雇员 k 倍交叉验证 (28 倍) 进行可靠的模型评估.
- 利用特征重要性分析来确定语音对性别分类的贡献.
主要成果:
- 这两种算法在按性别分类日本名字方面都取得了合理的准确性.
- 在分类准确度方面,XGBoost显著超过了随机森林算法.
- 确定了特定的音符: /m/和 /k/与女性性相关, /i/与男性性相关,与其他语言相反.
结论:
- 日语表现出独特的声音象征性性别表达,与典型的跨语言模式不同.
- XGBoost在捕捉复杂的语音-性别关系方面表现出卓越的能力.
- 调查结果强调了文化背景在声音象征中的重要性,并提供了对语言性别标志物的洞察.
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