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ZENODO
Article . 2019
License: CC BY
Data sources: ZENODO
Journal of Historical Linguistics
Article . 2019 . Peer-reviewed
Data sources: Crossref
MPG.PuRe
Article . 2019
Data sources: MPG.PuRe
MPG.PuRe
Article . 2019
Data sources: MPG.PuRe
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Detecting non-tree-like signal using multiple tree topologies

Authors: Verkerk, Annemarie;

Detecting non-tree-like signal using multiple tree topologies

Abstract

Abstract Recent applications of phylogenetic methods to historical linguistics have been criticized for assuming a tree structure in which ancestral languages differentiate and split up into daughter languages, while language evolution is inherently non-tree-like (François 2014; Blench 2015: 32–33). This article attempts to contribute to this debate by discussing the use of the multiple topologies method (Pagel & Meade 2006a) implemented in BayesPhylogenies (Pagel & Meade 2004). This method is applied to lexical datasets from four different language families: Austronesian (Gray, Drummond & Greenhill 2009), Sinitic (Ben Hamed & Wang 2006), Indo-European (Bouckaert et al. 2012), and Japonic (Lee & Hasegawa 2011). Evidence for multiple topologies is found in all families except, surprisingly, Austronesian. It is suggested that reticulation may arise from a number of processes, including dialect chain break-up, borrowing (both shortly after language splits and later on), incomplete lineage sorting, and characteristics of lexical datasets. It is shown that the multiple topologies method is a useful tool to study the dynamics of language evolution.

Country
Germany
Keywords

ddc:400, Austronesian, Bayesian phylogenetic inference, Sinitic, language contact, Indo-European, Japonic, reticulation, 400

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
7
Top 10%
Average
Top 10%
6
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bronze