Statistical methods fashionable again in Linguistics: Reconstructing Proto-Australian dialects

Reconstructing remote relationships – Proto-Australian noun class prefixation, by Mark Harvey & Robert Mailhammer, Diachronica (2017) 34(4): 470–515

Abstract:

Evaluation of hypotheses on genetic relationships depends on two factors: database size and criteria on correspondence quality. For hypotheses on remote relationships, databases are often small. Therefore, detailed consideration of criteria on correspondence quality is important. Hypotheses on remote relationships commonly involve greater geographical and temporal ranges. Consequently, we propose that there are two factors which are likely to play a greater role in comparing hypotheses of chance, contact and inheritance for remote relationships: (i) spatial distribution of corresponding forms;

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Evolutionary forces in language change depend on selective pressure, but also on random chance

english-language-evolution

A new interesting paper from Nature: Detecting evolutionary forces in language change, by Newberry, Ahern, Clark, and Plotkin (2017). Discovered via Science Daily.

The following are excerpts of materials related to the publication (written by Katherine Unger Baillie), from The University of Pennsylvania:

Examining substantial collections of annotated texts dating from the 12th to the 21st centuries, the researchers found that certain linguistic changes were guided by pressures analogous to natural selection — social, cognitive and other factors — while others seem to have occurred purely by happenstance.

“Linguists usually assume that when a change occurs in

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Forces driving grammatical change are different to those driving lexical change

Grammar change

A new paper at PNAS, Evolutionary dynamics of language systems, by Greenhill et al. (2017).

Significance

Do different aspects of language evolve in different ways? Here, we infer the rates of change in lexical and grammatical data from 81 languages of the Pacific. We show that, in general, grammatical features tend to change faster and have higher amounts of conflicting signal than basic vocabulary. We suggest that subsystems of language show differing patterns of dynamics and propose that modeling this rate variation may allow us to extract more signal, and thus trace language history deeper than has been previously

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