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.
Subjects by Vocabulary
Microsoft Academic Graph classification: Artificial intelligence business.industry business Southern china Computer science Task (project management) Natural language processing computer.software_genre computer Identification (information) Historical linguistics Computational linguistics Language evolution