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.
翻译:语言联系是一种普遍现象,表现在从捐赠者向接受者语言借用语言时,大多数借款探测方法都同等重要,尽管主要语言对遗产语言的影响大于其他语言。我们测试了在主要语言发挥重要作用的接触情况下进行词汇借款探测的新方法,对从西班牙语大量借用的七种拉丁美洲语言样本采用了两种古典顺序比较方法和一种机器学习方法。所有方法都表现良好,监督的机器学习系统优于经典系统。对检测错误的审查表明,如果考虑到与接受语言有不同含义的捐赠者语言,借款探测方法可以大大改进。