发表机构
NTU Singapore（新加坡南洋理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文旨在填补语言区域识别缺乏系统方法的空白，提出简单地理聚类方法，能识别任意区域分组，生成的全球宏观区域与现有分组相符，还为著名语言联盟生成局部分组。
AI 中文摘要
宏观区域是类型学研究中用于对感兴趣变量进行分组的地理区域。在语言类型学中，同一宏观区域内的语言被认为有接触的可能性。宏观区域与语系成员身份一起，被用作语言类型学模型的控制因素，以解决自相关问题。现有宏观区域很大程度上依赖专家确定，尚无系统方法识别给定区域的此类区域。本文试图填补这一空白，提出一种简单地理聚类方法来识别任意区域的分组，该方法生成的全球宏观区域与现有分组基本一致，还为著名语言联盟生成了局部分组。
英文摘要
Macroareas are geographical areas used in typological research for grouping variables of interest. In linguistic typology, languages in a given macroarea are considered to have potential for contact, in contrast to those outside the area, where contact is less likely. Along with language family membership, macroareas are used as controls for models in linguistic typology, in an attempt to address the problem of autocorrelation - the observation that historical developments or typological patterns may be due to contact between neighboring languages and/or inheritance from a common ancestral language. Macroareas are therefore a central aspect of research that seeks to separate universal properties of language from local (or language-specific) properties. Existing macroareas largely depend on expert determinations of what constitutes a geographical area of potential contact, and to date have mainly aligned with continents or landmasses (Hammarström and Donohue 2014; Nichols, Witzlack-Makarevich, and Bickel 2013). While there are various historical and theoretical reasons for these groupings, there as of yet has been no systematic approach to identifying such areas for a given region. This paper attempts to address such a gap and move beyond macroarea to identification of language areas of relatively arbitrary size, presenting a simple geographical clustering method for identifying groupings over any area. The method produces a set of worldwide macroareas that largely align with existing groupings, as well as local groupings for a well-known sprachbund.
