Similarity Dependent

Rama, Taraka

doi:10.18653/v1/k18-1027

Cited by 3 publications

References 30 publications

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Environmental factors affect the evolution of linguistic subgroups in Borneo

Smith

Rama

2022

DIA

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This study investigates the relatedness and history of the Austronesian languages of Borneo, which is the third largest island in the world and home to significant linguistic diversity. We apply Bayesian phylogenetic dating methods to lexical cognate data based on four historical calibration points to infer a dated phylogeny of 87 languages. The inferred tree topology agrees with the mid and lower-level subgrouping proposals based on the classical comparative method, but suggests a different higher-level organization. The root age of the dated tree is shallower than the archaeological estimates but agrees with a hypothesis of a past linguistic leveling event. The inferred homelands of the major linguistic subgroups from a Bayesian phylogeographic analysis agree with the homeland proposals from archaeology and linguistics. The inferred homelands for four of the eight subgroups support the riverine homeland hypothesis whereby the major linguistic subgroups developed initially in communities situated along Borneo’s major rivers.

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Environmental factors affect the evolution of linguistic subgroups in Borneo

Smith

Rama

2022

DIA

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A test of Generalized Bayesian dating: A new linguistic dating method

Rama

Wichmann

2020

PLoS ONE

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In current practice, when dating the root of a Bayesian language phylogeny the researcher is required to supply some of the information beforehand, including a distribution of root ages and dates for some nodes serving as calibration points. In addition to the potential subjectivity that this leaves room for, the problem arises that for many of the language families of the world there are no available internal calibration points. Here we address the following questions: Can a new Bayesian framework which overcomes these problems be introduced and how well does it perform? The new framework that we present is generalized in the sense that no family-specific priors or calibration points are needed. We moreover introduce a way to overcome another potential source of subjectivity in Bayesian tree inference as commonly practiced, namely that of manual cognate identification; instead, we apply an automated approach. Dates are obtained by fitting a Gamma regression model to tree lengths and known time depths for 30 phylogenetically independent calibration points. This model is used to predict the time depths of both the root and the internal nodes for 116 language families, producing a total of 1,287 dates for families and subgroups. It turns out that results are similar to those of published Bayesian studies of individual language families. The performance of the method is compared to automated glottochronology, which is an update of the classical method of Swadesh drawing upon automated cognate recognition and a new formula for deriving a time depth from percentages of shared cognates. It is also compared to a third dating method, that of the Automated Similarity Judgment Program (ASJP). In terms of errors and correlations with known dates, ASJP works better than the new method and both work better than automated glottochronology.

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An Automated Framework for Fast Cognate Detection and Bayesian Phylogenetic Inference in Computational Historical Linguistics

Rama¹,

List

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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We present a fully automated workflow for phylogenetic reconstruction on large datasets, consisting of two novel methods, one for fast detection of cognates and one for fast Bayesian phylogenetic inference. Our results show that the methods take less than a few minutes to process language families that have so far required large amounts of time and computational power. Moreover, the cognates and the trees inferred from the method are quite close, both to gold standard cognate judgments and to expert language family trees. Given its speed and ease of application, our framework is specifically useful for the exploration of very large datasets in historical linguistics.

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Similarity Dependent

Cited by 3 publications

References 30 publications

Environmental factors affect the evolution of linguistic subgroups in Borneo

Environmental factors affect the evolution of linguistic subgroups in Borneo

A test of Generalized Bayesian dating: A new linguistic dating method

An Automated Framework for Fast Cognate Detection and Bayesian Phylogenetic Inference in Computational Historical Linguistics

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