English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

Representing and computing uncertainty in phonological reconstruction

MPS-Authors
/persons/resource/persons201886

List,  Johann-Mattis       
Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

/persons/resource/persons96313

Forkel,  Robert       
Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

/persons/resource/persons274898

Blum,  Frederic       
Department of Linguistic and Cultural Evolution, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
Supplementary Material (public)
There is no public supplementary material available
Citation

List, J.-M., Hill, N., Forkel, R., & Blum, F. (2023). Representing and computing uncertainty in phonological reconstruction. In N. Tahmasebi, S. Montariol, H. Dubossarsky, A. Kutuzov, S. Hengchen, D. Alfter, et al. (Eds.), Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change (pp. 22-32). Association for Computational Linguistics.


Cite as: https://hdl.handle.net/21.11116/0000-000E-0329-1
Abstract
Despite the inherently fuzzy nature of reconstructions in historical linguistics, most scholars do not represent their uncertainty when proposing proto-forms. With the increasing success of recently proposed approaches to automating certain aspects of the traditional comparative method, the formal representation of proto-forms has also improved. This formalization makes it possible to address both the representation and the computation of uncertainty. Building on recent advances in supervised phonological reconstruction, during which an algorithm learns how to reconstruct words in a given proto-language relying on previously annotated data, and inspired by improved methods for automated word prediction from cognate sets, we present a new framework that allows for the representation of uncertainty in linguistic reconstruction and also includes a workflow for the computation of fuzzy reconstructions from linguistic data.