Alexandria Digital Research Library

Machine learning in language reconstruction : A-Star models of sound change

Author:
Schaefer, Kevin Jay
Degree Grantor:
University of California, Santa Barbara. Linguistics
Degree Supervisor:
Fermin Moscoso del Prado Martin
Place of Publication:
[Santa Barbara, Calif.]
Publisher:
University of California, Santa Barbara
Creation Date:
2016
Issued Date:
2016
Topics:
Linguistics
Keywords:
Historical reconstruction
Historical linguistics
A*.
Proto-language
Genres:
Dissertations, Academic and Online resources
Dissertation:
M.A.--University of California, Santa Barbara, 2016
Description:

Research in computational methods has focused on phylogenetic taxonomy, cognate recognition, phone alignment, and even identification of correspondence sets, but more work is to be done in computer-assisted modeling of diachronic sound change. An adaptation of A*, a path-finding algorithm relying on cost and distance-to-goal heuristics, was used in combination with data from comparative dictionaries to model sound change applying typologically-based predictions about sound change. Typological data serves here as a quantitative proxy for factors driving sound change. As long as the data to be modeled include only sound changes, both unconditioned or conditioned by contact with other phones (coarticulatory effects), but exclude any unconditioned mergers or classes of phones not included in the sample (e.g., click consonants), the algorithm can, for automatically generated data, model a proto-language lexical reconstruction and the ordered sequences of conditioned sound changes required to produce the input reflexes.

Physical Description:
1 online resource (42 pages)
Format:
Text
Collection(s):
UCSB electronic theses and dissertations
ARK:
ark:/48907/f3w95985
ISBN:
9781369146684
Catalog System Number:
990046969060203776
Rights:
Inc.icon only.dark In Copyright
Copyright Holder:
Kevin Schaefer
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