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Using hybridization networks to retrace the evolution of Indo-European languages

Overview of attention for article published in BMC Ecology and Evolution, September 2016
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Title
Using hybridization networks to retrace the evolution of Indo-European languages
Published in
BMC Ecology and Evolution, September 2016
DOI 10.1186/s12862-016-0745-6
Pubmed ID
Authors

Matthieu Willems, Etienne Lord, Louise Laforest, Gilbert Labelle, François-Joseph Lapointe, Anna Maria Di Sciullo, Vladimir Makarenkov

Abstract

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.

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X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 3%
Sweden 1 3%
Unknown 29 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Student > Bachelor 5 16%
Researcher 5 16%
Student > Doctoral Student 4 13%
Professor > Associate Professor 3 10%
Other 5 16%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 32%
Linguistics 6 19%
Arts and Humanities 2 6%
Physics and Astronomy 2 6%
Engineering 2 6%
Other 7 23%
Unknown 2 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 November 2019.
All research outputs
#7,714,565
of 25,374,647 outputs
Outputs from BMC Ecology and Evolution
#1,760
of 3,714 outputs
Outputs of similar age
#111,298
of 344,900 outputs
Outputs of similar age from BMC Ecology and Evolution
#47
of 87 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 3,714 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 344,900 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 87 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.