论文标题
不同的空间，时间和语法量表的语言统计数据
Language statistics at different spatial, temporal, and grammatical scales
论文作者
论文摘要
近几十年来，随着数据的可用，统计语言学已大大提高。这使研究人员能够研究语言的统计特性如何随着时间而变化。在这项工作中，我们使用Twitter的数据来探索英语和西班牙语，考虑到不同尺度的等级多样性：时间（从3到96小时），空间（从3公里到3000+km Radii）和语法（从字母组合到Pentagrams）。我们发现这三个量表都是相关的。但是，最大的变化来自语法量表的变化。在最低的语法量表（会标）上，排名多样性曲线最相似，独立于其他量表，语言和国家的价值。随着语法量表的增长，等级多样性曲线的变化更大，具体取决于时间和空间量表以及语言和国家。我们还研究了Twitter特定令牌的统计数据：表情符号，主题标签和用户提及。这些特殊类型的令牌表现出一种sigmoid的行为作为等级多样性函数。我们的结果有助于量化似乎普遍存在的语言统计数据的各个方面，这可能导致变化。
Statistical linguistics has advanced considerably in recent decades as data has become available. This has allowed researchers to study how statistical properties of languages change over time. In this work, we use data from Twitter to explore English and Spanish considering the rank diversity at different scales: temporal (from 3 to 96 hour intervals), spatial (from 3km to 3000+km radii), and grammatical (from monograms to pentagrams). We find that all three scales are relevant. However, the greatest changes come from variations in the grammatical scale. At the lowest grammatical scale (monograms), the rank diversity curves are most similar, independently on the values of other scales, languages, and countries. As the grammatical scale grows, the rank diversity curves vary more depending on the temporal and spatial scales, as well as on the language and country. We also study the statistics of Twitter-specific tokens: emojis, hashtags, and user mentions. These particular type of tokens show a sigmoid kind of behaviour as a rank diversity function. Our results are helpful to quantify aspects of language statistics that seem universal and what may lead to variations.
