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.
imposes an additional cutoff based on the PMI scoring matrix. Further, also employs the PMI scoring matrix to infer family trees for new language families and compares those trees with the trees given in @cite_17 . take a slightly different approach, in that, the authors compute a PMI matrix independently for each language family and evaluate its performance at the task of pair-wise cognate identification. In this work, we also compare the convolutional networks against PMI based binary classifier.
@cite_17
1923658367
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.