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Theoretical Linguistics
Article . 2019 . Peer-reviewed
License: CC BY
Data sources: Crossref
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https://dx.doi.org/10.48550/ar...
Article . 2018
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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Computational historical linguistics

Authors: Gerhard Jäger;

Computational historical linguistics

Abstract

AbstractComputational approaches to historical linguistics have been proposed for half a century. Within the last decade, this line of research has received a major boost, owing both to the transfer of ideas and software from computational biology and to the release of several large electronic data resources suitable for systematic comparative work. In this article, some of the central research topics of this new wave of computational historical linguistics are introduced and discussed. These areautomatic assessment of genetic relatedness,automatic cognate detection,phylogenetic inferenceandancestral state reconstruction. They will be demonstrated by means of a case study of automatically reconstructing a Proto-Romance word list from lexical data of 50 modern Romance languages and dialects. The results illustrate both the strengths and the weaknesses of the current state of the art of automating the comparative method.

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Keywords

FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)

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citations
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!
13
Top 10%
Average
Top 10%
Green
hybrid