Using sequence similarity networks to identify partial cognates in multilingual wordlists

Abstract

Increasing amounts of digital data in historical linguistics necessitate the development of automatic methods for the detection of cognate words across languages. Recently developed methods work well on language families with moderate time depths, but they are not capable of identifying cognate morphemes in words which are only partially related. Partial cognacy, however, is a frequently recurring phenomenon, especially in language families with productive derivational morphology. This paper presents a pilot approach for partial cognate detection in which networks are used to represent similarities between word parts and cognate morphemes are identified with help of state-of-theart algorithms for network partitioning. The approach is tested on a newly created benchmark dataset with data from three sub-branches of Sino-Tibetan and yields very promising results, outperforming all algorithms which are not sensible to partial cognacy.