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.

研究探讨了语言接触中主导语言对被介入语言的影响，通过使用三种方法（其中包括两种传统的序列比较方法和一种机器学习方法）在拉丁美洲七种常常和西班牙语有大量交流和借词的语言中测试新型的词汇借用检测方法，并通过检测中的误差反映出通过考虑来自受体语言不同意义的捐赠词汇可以大大提高借贷检测的精度。

在多语言词列表中检测主流语言的词汇借用

Detecting Lexical Borrowings from Dominant Languages in Multilingual Wordlists

Computational approaches in historical linguistics have been increasingly
applied during the past decade and many new methods that implement parts of the
traditional comparative method have been proposed. Despite these increased
efforts, there are not many easy-to-use and fast approaches for the task of
phonological reconstruction. Here we present a new framework that combines
state-of-the-art techniques for automated sequence comparison with novel
techniques for phonetic alignment analysis and sound correspondence pattern
detection to allow for the supervised reconstruction of word forms in ancestral
languages. We test the method on a new dataset covering six groups from three
different language families. The results show that our method yields promising
results while at the same time being not only fast but also easy to apply and
expand.

本文介绍了一种新的计算机模型，该模型结合了最先进的自动化序列比对技术和新颖的语音对准分析技术以及声音对应模式检测技术，可用于监督祖先语言的单词形态学恢复，测试结果表明该方法既快速又易于实现和扩展。