In this paper, we explore the use of convolutional networks (ConvNets) for the purpose of cognate identification. We compare our architecture with binary classifiers based on string similarity measures on different language families. Our experiments show that convolutional networks achieve competitive results across concepts and across language families at the task of cognate identification.
This paper describes the Glottolog Langdoc project, an attempt to provide near-total bibliographical coverage of descriptive resources to the world's languages. Every reference is treated as a resource, as is every "languoid"[1]. References are linked to the languoids which they describe, and languoids are linked to the references described by them. Family relations between languoids are modeled in SKOS, as are relations across different classifications of the same languages. This setup allows the representation of languoids as collections of references, rendering the question of the definition of entities like 'Scots', 'West-Germanic' or 'Indo-European' more empirical.