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：语言接触是一种普遍现象，体现在词汇从施借语言向受借语言的借用中。大多数计算性的借用检测方法将所研究的所有语言视为同等重要，尽管主导语言对传承语言的影响远强于反向影响。本文针对主导语言发挥重要作用的接触情境，测试了词汇借用检测的新方法：将两种经典序列比较方法与一种机器学习方法应用于七种拉丁美洲语言样本（这些语言均从西班牙语大量借用词汇）。所有方法均表现良好，其中监督式机器学习系统的性能优于经典系统。对检测错误的回顾表明，若考虑施借词与受借词之间可能存在的语义分歧，借用检测效果可得到显著提升。