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

Ryan Nichols, Edward Slingerland, Kristoffer Laigaard Nielbo, Uffe Bergeton, Carson Logan, Scott Kleinman

Research output: Contribution to journalJournal articleResearchpeer-review

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.
Original languageEnglish
JournalJournal of Asian Studies
Volume77
Issue number1
Pages (from-to)19–57
ISSN0021-9118
DOIs
Publication statusPublished - Feb 2018

Fingerprint

Dive into the research topics of 'Modeling the Contested Relationship between Analects, Mencius, and Xunzi: Preliminary Evidence from a Machine-Learning Approach'. Together they form a unique fingerprint.

Cite this