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Modeling the Contested Relationship between Analects, Mencius, and Xunzi: Preliminary Evidence from a Machine-Learning Approach

Published online by Cambridge University Press:  19 February 2018

Ryan Nichols
Affiliation:
Ryan Nichols (rnichols@fullerton.edu) is Associate Professor in the Department of Philosophy at California State University, Fullerton, and Research Affiliate of the University of British Columbia's Centre for Human Evolution, Cognition, and Culture.
Edward Slingerland
Affiliation:
Edward Slingerland (edward.slingerland@ubc.ca) is Professor in the Department of Asian Studies, University of British Columbia.
Kristoffer Nielbo
Affiliation:
Kristoffer Nielbo (kln@cas.au.dk) is History Researcher in the datakube, University of Southern Denmark, and Researcher at the Interacting Minds Centre, Aarhus University.
Uffe Bergeton
Affiliation:
Uffe Bergeton (bergeton@email.unc.edu) is Assistant Professor in the Department of Asian Studies, University of North Carolina.
Carson Logan
Affiliation:
Carson Logan (carsonklogan@gmail.com) is Developer at Tuangru in Vancouver, Canada.
Scott Kleinman
Affiliation:
Scott Kleinman (scott.kleinman@csun.edu) is Professor in the Department of English, California State University, Northridge.

Abstract

This article presents preliminary findings from a multi-year, multi-disciplinary text analysis project using an ancient and medieval Chinese corpus of over five million characters in works that date from the earliest received texts to the Song dynasty. It describes “distant reading” methods in the humanities and the authors’ corpus; introduces topic-modeling procedures; answers questions about the authors’ data; discusses complementary relationships between machine learning and human expertise; explains topics represented in Analects, Mencius, and Xunzi that set each of those texts apart from the other two; and explains topics that intersect all three texts. The authors’ results confirm many scholarly opinions derived from close-reading methods, suggest a reappraisal of Xunzi’s shared semantic content with Analects, and yield several actionable research questions for traditional scholarship. The aim of this article is to initiate a new conversation about implications of machine learning for the study of Asian texts.

Type
Research Article
Copyright
Copyright © The Association for Asian Studies, Inc. 2018 

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