Computational Historical Linguistics
This work addresses the challenge of efficiently analyzing linguistic evolution for researchers in historical linguistics, though it appears incremental as it builds on existing computational approaches from biology and leverages new data resources.
The paper tackles the problem of automating historical linguistic analyses, such as assessing genetic relatedness and reconstructing ancestral languages, by applying computational methods to large datasets, with a case study demonstrating the automatic reconstruction of a Proto-Romance word list from 50 modern Romance languages and dialects.
Computational approaches to historical linguistics have been proposed since half a century. Within the last decade, this line of research has received a major boost, owing both to the transfer of ideas and software from computational biology and to the release of several large electronic data resources suitable for systematic comparative work. In this article, some of the central research topic of this new wave of computational historical linguistics are introduced and discussed. These are automatic assessment of genetic relatedness, automatic cognate detection, phylogenetic inference and ancestral state reconstruction. They will be demonstrated by means of a case study of automatically reconstructing a Proto-Romance word list from lexical data of 50 modern Romance languages and dialects.