Using hybridization networks to retrace the evolution of Indo-European languages.
Curious parallels between the processes of species and language evolution have been observed by many researchers. Retracing the evolution of Indo-European (IE) languages remains one of the most intriguing intellectual challenges in historical linguistics. Most of the IE language studies use the traditional phylogenetic tree model to represent the evolution of natural languages, thus not taking into account reticulate evolutionary events, such as language hybridization and word borrowing which can be associated with species hybridization and horizontal gene transfer, respectively. More recently, implicit evolutionary networks, such as split graphs and minimal lateral networks, have been used to account for reticulate evolution in linguistics.



Striking parallels existing between the evolution of species and natural languages allowed us to apply three computational biology methods for reconstruction of phylogenetic networks to model the evolution of IE languages. We show how the transfer of methods between the two disciplines can be achieved, making necessary methodological adaptations. Considering basic vocabulary data from the well-known Dyen's lexical database, which contains word forms in 84 IE languages for the meanings of a 200-meaning Swadesh list, we adapt a recently developed computational biology algorithm for building explicit hybridization networks to study the evolution of IE languages and compare our findings to the results provided by the split graph and galled network methods.



We conclude that explicit phylogenetic networks can be successfully used to identify donors and recipients of lexical material as well as the degree of influence of each donor language on the corresponding recipient languages. We show that our algorithm is well suited to detect reticulate relationships among languages, and present some historical and linguistic justification for the results obtained. Our findings could be further refined if relevant syntactic, phonological and morphological data could be analyzed along with the available lexical data.
Department of Computer Science, Université du Québec à Montréal, Case postale 8888, succursale Centre-ville, Montréal, Québec, H3C 3P8, Canada.
Matthieu
M
Willems
Department of Computer Science, Université du Québec à Montréal, Case postale 8888, succursale Centre-ville, Montréal, Québec, H3C 3P8, Canada.|Department of Biological Sciences, Université de Montréal, C.P. 6128 succ. Centre-Ville, Montreal, Quebec, H3C 3J7, Canada.
Etienne
E
Lord
Department of Computer Science, Université du Québec à Montréal, Case postale 8888, succursale Centre-ville, Montréal, Québec, H3C 3P8, Canada.
Louise
L
Laforest
Department of Mathematics, Université du Québec à Montréal, Case postale 8888, succursale Centre-ville, Montréal, Québec, H3C 3P8, Canada.
Gilbert
G
Labelle
Department of Biological Sciences, Université de Montréal, C.P. 6128 succ. Centre-Ville, Montreal, Quebec, H3C 3J7, Canada.
François-Joseph
FJ
Lapointe
Department of Linguistics, Université du Québec à Montréal, Case postale 8888, succursale Centre-ville, Montréal, Québec, H3C 3P8, Canada.
Anna Maria
AM
Di Sciullo
Department of Computer Science, Université du Québec à Montréal, Case postale 8888, succursale Centre-ville, Montréal, Québec, H3C 3P8, Canada. makarenkov.vladimir@uqam.ca.
Vladimir
V
Makarenkov
D000465:Algorithms; D019295:Computational Biology; D016208:Databases, Factual; D005060:Europe; D007194:India; D007802:Language; D008037:Linguistics; D008962:Models, Theoretical; D010802:Phylogeny
Historical linguistics; Phylogenetic networks; Phylogenetic trees; Reticulate evolution