Abstract
Language contact is a pervasive phenomenon reflected in the borrowing of words from donor to recipient languages. Most computational approaches to borrowing detection treat all languages under study as equally important, even though dominant languages have a stronger impact on heritage languages than vice versa. We test new methods for lexical borrowing detection in contact situations where dominant languages play an important role, applying two classical sequence comparison methods and one machine learning method to a sample of seven Latin American languages which have all borrowed extensively from Spanish. All methods perform well, with the supervised machine learning system outperforming the classical systems. A review of detection errors shows that borrowing detection could be substantially improved by taking into account donor words with divergent meanings from recipient words.
Abstract (translated)
语言接触是一个普遍存在的现象,体现在从捐赠者到接收者语言的词汇借用方面。大多数计算词汇借用检测的方法都将其研究的所有语言视为同等重要的,尽管主导语言对遗产语言的影响比反之更强。我们在主导语言占主导地位的接触情况下测试了一些新的词汇借用检测方法,应用了两个经典序列比较方法和一个机器学习方法对一个样本中的七个拉丁美洲语言,这些语言都广泛地从西班牙借用。所有方法都表现良好, supervised 机器学习系统比经典系统表现更好。对检测错误的回顾表明,考虑捐赠词和接收词之间差异很大的捐赠词可以显著改善词汇借用检测。
URL
https://arxiv.org/abs/2302.00189
