在不同的空间,时间和语法尺度上的语言统计
Fernanda Sánchez-Puig1,2,3, Rogelio Lozano-Aranda1,2, Dante Pérez-Méndez2,4
1Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.
Entropy (Basel, Switzerland)
|September 27, 2024
概括
这项研究分析了推特上的英语和西班牙语,发现单词模式 (ngrams) 与语法复杂性差异最大. 阶级多样性显示出普遍的趋势,但国家和时间因素影响语言使用.
科学领域:
- 计算语言学 计算语言学
- 量化语言学 量化语言学
- 社会语言学 社会语言学
背景情况:
- 大数据集的可用性推动了统计语言学.
- 推特数据为分析实时语言使用提供了丰富的资源.
研究的目的:
- 用Twitter数据调查英语和西班牙语的排名多样性.
- 检查时间,空间和语法尺度对语言变化的影响.
- 量化通用语言统计数据,并确定变化的来源.
主要方法:
- 分析了来自八个国家的2014年Twitter数据.
- 在时间 (3-96小时) 和空间 (3公里-3000公里) 尺度上对单词ngrams (1克至5克) 的研究.
- 检查Twitter特定代币 (表情符号,标签,提及) 的排名多样性曲线和统计属性.
主要成果:
- 排名多样性显示了跨语言,国家和尺度的1克级别的相似性.
- 增加的语法复杂性 (更高的ngrams) 会导致更明显的变化,受到时间,空间,语言和国家因素的影响.
- 特定于Twitter的代币在其等级多样性函数中表现出一个sigmoid模式.
结论:
- 语法尺度是语言排名多样性变化的关键驱动力.
- 虽然存在普遍的模式,但Twitter上的语言使用是由上下文 (时间,位置,国籍) 塑造的.
- 该研究量化了语言统计数据,并突出了影响数字通信语言多样性的因素.
关键词:
他们的推特是Twitter.复杂性的复杂性 复杂性的复杂性地理定位的地理位置.语言模型语言模型恩格拉姆斯是什么意思排名 多样性 多样性缩放规则的法律,缩放规则的法律.统计 统计 统计 统计 统计更多相关视频
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