Contact us
 |
[email protected] |
 |
3275638434 |
 |
 |
| Paper Publishing WeChat |
Useful Links
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
Author(s)
HU Jia-jia
Full-Text PDF
XML 301 Views
DOI:10.17265/2159-5836/2019.03.006
Affiliation(s)
Beijing Normal University, Beijing, China
ABSTRACT
The Analects, Mengzi and Xunzi are the top-three classical works of pre-Qin Confucianism, which epitomized thoughts and ideas of Confucius, Mencius and XunKuang . There have been lots of spirited and in-depth discussions on their ideological inheritance and development from all kinds of academics. This paper tries to cast a new light on these discussions through “machine reading”
KEYWORDS
pre-Qin Confucianism, the Analects, Mengzi, Xunzi, text analysis, machine reading, topic modeling, Mallet, Gephi
Cite this paper
References
Brett, M. R. (2012). Topic modeling: A basic introduction. Journal of Digital Humanities, 2, 1.
Graham, S., Weingart, S., & Milligan, I. (2012). Getting started with topic modeling and MALLET. Programming Historian, 2, 12. http://programminghistorian.org/lessons/topic-modeling-and-mallet
McCallum, A. K. (2002). MALLET: A machine learning for language toolkit. http://mallet.cs.umass.edu
Riddell, A. B. (2012). How to read 22198 journal articles: Studying the history of German studies with topic models. https://ariddell.org/static/how-to-read-n-articles.pdf
Steyvers, M., & Griffiths, T. (2007). Probabilistic topic models. In T. Landauer, D. McNamara, S. Dennis, and W. Kintsch (Eds.), Latent semantic analysis: A road to meaning (pp. 1-15). Hillsdale, NJ: Laurence Erlbaum.
Graham, S., Weingart, S., & Milligan, I. (2012). Getting started with topic modeling and MALLET. Programming Historian, 2, 12. http://programminghistorian.org/lessons/topic-modeling-and-mallet
McCallum, A. K. (2002). MALLET: A machine learning for language toolkit. http://mallet.cs.umass.edu
Riddell, A. B. (2012). How to read 22198 journal articles: Studying the history of German studies with topic models. https://ariddell.org/static/how-to-read-n-articles.pdf
Steyvers, M., & Griffiths, T. (2007). Probabilistic topic models. In T. Landauer, D. McNamara, S. Dennis, and W. Kintsch (Eds.), Latent semantic analysis: A road to meaning (pp. 1-15). Hillsdale, NJ: Laurence Erlbaum.