Rooting of phylogenetic trees is an important task, not only in evolutionary biology, but also in historical linguistics. So far, however, different rooting methods have not yet been sufficiently tested on linguistics data. Given that a new method for the automatic rooting of phylogenetic trees has been presented recently in biology, it seemed to be a good occasion to test in detail how well this new method works in comparison with alternative methods.
Monthly Archives: May 2019
Biological metaphors and methods in historical linguistics (1): Introduction
Evolutionary biology and historical linguistics share a long history of scientific exchange, reflected both not only in the sharing and transfer of metaphors but more recently also in the transfer of methods. Already Charles Darwin claimed that both species and languages evolve in tree-like patterns, and linguists, like Wilhelm Meyer-Lübke, used terms like “sprachliche[n] Biologie” (‘linguistic biology’) when referring to the history of languages (Meyer-Lübke 1890, x). While the discipline of historical-comparative linguistics allowed biologists to adopt evolutionary thought against religious dogma in the late 19th century (Wells 1987: 54), it was biological applications which opened up the possibility of large quantitative studies in linguistics based on computational approaches (Geisler and List 2013, 111).
Behind the Sino-Tibetan Database of Lexical Cognates: Introductory remarks
One of the major efforts behind our recently published paper on the origin and spread of the Sino-Tibetan languages (Sagart et al. 2019) was the creation of a database of lexical cognates which was used to run the phylogenetic analyses. The creation of this database started about four years ago, when I joined the Centre des Recherches Linguistiques sur l’Asie Oriental in Paris as a research fellow in January 2015, and Guillaume Jacques and Laurent Sagart approached me with the idea of making a phylogenetic study of Sino-Tibetan languages. In December 2017, almost three years after having started, our database consisted of 180 concepts translated into 50 different languages. Since creating the database was not directly straightforward from the beginning, with quite a few situations in which we realized we had to re-arrange the data or the procedure, I thought it might be useful to share our experience in a series of blog posts, as it might be interesting for scholars who wish to create their own database.