English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Studying language evolution in the age of big data

Bhattacharya, T., Retzlaff, N., Blasi, D. E., Croft, W., Cysouw, M., Hruschka, D., et al. (2018). Studying language evolution in the age of big data. Journal of Language Evolution, 3(2), 94-129. doi:10.1093/jole/lzy004.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0002-1C5D-8 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-1C5E-7
Genre: Journal Article

Files

show Files
hide Files
:
shh1011.pdf (Publisher version), 2MB
 
File Permalink:
-
Name:
shh1011.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Bhattacharya, Tanmoy, Author
Retzlaff, Nancy, Author
Blasi, Damián E.1, Author              
Croft, William, Author
Cysouw, Michael, Author
Hruschka, Daniel, Author
Maddieson, Ian, Author
Müller, Lydia, Author
Smith, Eric, Author
Stadler, Peter F, Author
Starostin, George, Author
Youn, Hyejin, Author
Affiliations:
1Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society, ou_2074311              

Content

show
hide
Free keywords: -
 Abstract: The increasing availability of large digital corpora of cross-linguistic data is revolutionizing many branches of linguistics. Overall, it has triggered a shift of attention from detailed questions about individual features to more global patterns amenable to rigorous, but statistical, analyses. This engenders an approach based on successive approximations where models with simplified assumptions result in frameworks that can then be systematically refined, always keeping explicit the methodological commitments and the assumed prior knowledge. Therefore, they can resolve disputes between competing frameworks quantitatively by separating the support provided by the data from the underlying assumptions. These methods, though, often appear as a ‘black box’ to traditional practitioners. In fact, the switch to a statistical view complicates comparison of the results from these newer methods with traditional understanding, sometimes leading to misinterpretation and overly broad claims. We describe here this evolving methodological shift, attributed to the advent of big, but often incomplete and poorly curated data, emphasizing the underlying similarity of the newer quantitative to the traditional comparative methods and discussing when and to what extent the former have advantages over the latter. In this review, we cover briefly both randomization tests for detecting patterns in a largely model-independent fashion and phylolinguistic methods for a more model-based analysis of these patterns. We foresee a fruitful division of labor between the ability to computationally process large volumes of data and the trained linguistic insight identifying worthy prior commitments and interesting hypotheses in need of comparison.

Details

show
hide
Language(s): eng - English
 Dates: 2018-06-082018-07
 Publication Status: Published in print
 Pages: 36
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1093/jole/lzy004
Other: shh1011
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Journal of Language Evolution
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 3 (2) Sequence Number: - Start / End Page: 94 - 129 Identifier: ISSN: 2058-458X
CoNE: https://pure.mpg.de/cone/journals/resource/journals