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Language family analysis and geocomputation

Crawford, Daniel (2020) Language family analysis and geocomputation. Undergraduate Thesis, University of Pittsburgh. (Unpublished)

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Abstract

With the fast-growing pace of advancements in computer science, mathematics, and linguistics, great strides have been made in each field. Here, work regarding the analysis of language families will be presented in an argument for the acceptance of results that are derived from a computational means. Specially, this research leverages machine learning methodologies to gain insight into the relationship between, and classification of, different languages and language families. Further, the higher rate of the availability of data regarding the geospatial aspects of a language spreading allows for the incorporation of this data into an analysis of language spread. This research lays the foundation and establishes a framework in which these two aspects, computational analysis and geospatial data, are intertwined to offer a perspective and glean insight into language.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Crawford, Danieldac187@pitt.edudac187
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWheeler, Jeffreyjwheeler@pitt.edujwheeler
Committee MemberDice, Lauralaurad@pitt.edulaurad
Committee MemberHan, Na-Raenareahan@pitt.edunaraehan
Committee MemberSchneier, Michaelmhs64@pitt.edumhs64
Date: 4 May 2020
Date Type: Publication
Defense Date: 16 April 2020
Approval Date: 4 May 2020
Submission Date: 17 April 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 43
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Mathematics
University Honors College
Degree: BS - Bachelor of Science
Thesis Type: Undergraduate Thesis
Refereed: Yes
Uncontrolled Keywords: phylogenetic analysis, linguistics, clustering, language family, gradient descent, machine learning, R
Date Deposited: 04 May 2020 15:29
Last Modified: 04 May 2020 15:42
URI: http://d-scholarship.pitt.edu/id/eprint/38738

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