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Automated identification of borrowings in multilingual wordlists
Copyright policy )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.
Microsoft Academic Graph classification: Historical linguistics Computer science Natural language processing computer.software_genre computer Computational linguistics Southern china Language evolution Identification (information) Task (project management) Artificial intelligence business.industry business
Research Article, Articles, computational linguistics, historical linguistics, lexical borrowing, borrowing detection, computational historical linguistics, General Medicine
Research Article, Articles, computational linguistics, historical linguistics, lexical borrowing, borrowing detection, computational historical linguistics, General Medicine
Microsoft Academic Graph classification: Historical linguistics Computer science Natural language processing computer.software_genre computer Computational linguistics Southern china Language evolution Identification (information) Task (project management) Artificial intelligence business.industry business
- Max Planck Society Germany
- Max Planck Society Germany
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.