Abstract
The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89% for the best-performing method Infomap. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection—although not perfect—could become an important component of future research in historical linguistics.
Highlights
Historical linguistics is currently facing a dramatic increase in digitally available datasets [1,2,3,4,5]
The results are generally consistent with those reported by List [19] for the performance of Turchin, Edit Distance, Sound-Class Based Alignment doi:10.1371/journal.pone.0170046.g001 (SCA), and LexStat: The Turchin method is very conservative with a low amount of false positives as reflected by the high precision, but a very large amount of undetected cognate relations as reflected by the low recall
The SCA method outperforms the Edit Distance, showing that refined distance scores can make a certain difference in automatic cognate detection
Summary
Historical linguistics is currently facing a dramatic increase in digitally available datasets [1,2,3,4,5]. There are too few expert historical linguists to analyse the world’s more than 7500 languages [7] and, only a small percentage of these languages have been thoroughly investigated leaving us in the dark about their history and relationships. This becomes especially evident in largely understudied linguistic areas like New Guinea, parts of South America, or the Himalayan region, and our lack of knowledge about these languages has immediate implications for our understanding of human prehistory.
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