A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns

Johann-Mattis List, Robert Forkel, Nathan Hill


Abstract
Computational approaches in historical linguistics have been increasingly applied during the past decade and many new methods that implement parts of the traditional comparative method have been proposed. Despite these increased efforts, there are not many easy-to-use and fast approaches for the task of phonological reconstruction. Here we present a new framework that combines state-of-the-art techniques for automated sequence comparison with novel techniques for phonetic alignment analysis and sound correspondence pattern detection to allow for the supervised reconstruction of word forms in ancestral languages. We test the method on a new dataset covering six groups from three different language families. The results show that our method yields promising results while at the same time being not only fast but also easy to apply and expand.
Anthology ID:
2022.lchange-1.9
Volume:
Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
LChange
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
89–96
Language:
URL:
https://aclanthology.org/2022.lchange-1.9
DOI:
10.18653/v1/2022.lchange-1.9
Bibkey:
Cite (ACL):
Johann-Mattis List, Robert Forkel, and Nathan Hill. 2022. A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns. In Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change, pages 89–96, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
A New Framework for Fast Automated Phonological Reconstruction Using Trimmed Alignments and Sound Correspondence Patterns (List et al., LChange 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lchange-1.9.pdf
Video:
 https://aclanthology.org/2022.lchange-1.9.mp4
Code
 lingpy/supervised-reconstruction-paper