使用神经网络对语言变化的进化力进行分类
Folgert Karsdorp1, Enrique Manjavacas2, Lauren Fonteyn3
1Royal Netherlands Academy of Arts and Sciences, Meertens Institute, Amsterdam, The Netherlands.
Evolutionary human sciences
|August 17, 2023
概括
研究人员开发了一种新的深度神经网络技术,以区分中性进化与语言和文化变化的选择压力. 这种方法准确地识别时间序列数据中的独特模式,比现有的统计测试提供了改进.
科学领域:
- 计算语言学计算语言学
- 进化生物学是进化的生物学.
- 文化演变的文化演变.
背景情况:
- 从选择压力中区分中性进化 (随机偏移) 是研究语言和文化变化的关键挑战.
- 现有的方法,如频率增量测试,在准确识别这些进化力方面面临局限性.
研究的目的:
- 介绍一种基于深度神经网络的新技术,用于检测文化变化中的进化力量.
- 为了提高准确性,将进化力检测重新制定为二进制分类任务.
主要方法:
- 利用深度神经网络,特别是用于时间序列分析的残余网络.
- 在人工生成的文化变化数据样本上训练模型.
- 将神经网络模型的性能与频率增量测试进行了比较.
主要成果:
- 深度神经网络技术高效准确地学会了区分随机漂移和选择压力.
- 该模型在确定每个进化过程的独特时间序列方面表现一致.
- 神经时间序列分类系统解决了频率增量测试的主要局限性.
结论:
- 深度神经网络为分析文化和语言变化的进化力量提供了强大而准确的方法.
- 这种新技术为区分中性漂移和选择驱动的变化提供了强大的解决方案.
- 这项研究强调了机器学习在促进对文化演变的理解方面的潜力.
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