Author ORCID Identifier

Date Available


Year of Publication


Degree Name

Master of Arts in Linguistic Theory and Typology (MALTT)

Document Type

Master's Thesis


Arts and Sciences



First Advisor

Dr. Andrew Byrd


The disambiguation of loanwords and cognates can be a challenge, especially in areas where there has been intense language contact over an extended period of time, when the contact is between genetically related languages, and when the number of languages involved is large Over the past several decades, more and more computational approaches to automatic cognate and borrowing detection have been created in an attempt to ease the load of examining hundreds to thousands of individual lexemes, as well as determine language family relationships with allegedly greater accuracy. While these methods are not perfect and cannot replace the knowledge or skillset of a linguist,, this paper seeks to apply a computer-assisted, as opposed to purely computational, approach to lexical borrowing detection to three Northeast Caucasian languages spoken in a cluster of villages in Dagestan: Avar, Lak, and Archi. In this thesis, I utilize computational methods for cognate detection as a starting point, as well as a lexical distribution approach to borrowing, followed by qualitative methods for determining loanwords from borrowings as applied to the output of the computational methods.

Digital Object Identifier (DOI)