Automated identification of borrowings in multilingual wordlists [version 3; peer review: 4 approved]

Abstract

Although lexical borrowing is an important aspect of language evolution, there have been few attempts to automate the identification of borrowings in lexical datasets. Moreover, none of the solutions which have been proposed so far identify borrowings across multiple languages. This study proposes a new method for the task and tests it on a newly compiled large comparative dataset of 48 South-East Asian languages from Southern China. The method yields very promising results, while it is conceptually straightforward and easy to apply. This makes the approach a perfect candidate for computer-assisted exploratory studies on lexical borrowing in contact areas.

Keywords

• computational linguistics
• historical linguistics
• lexical borrowing
• borrowing detection
• computational historical linguistics
• eng