SM  Vol.3 No.2 , April 2013
Study of Pauline Epistles in the New Testament Using Machine Learning
Author(s) Wei Hu
ABSTRACT

The New Testament of the Bible is a collection of 27 books, 13 of which are attributed to St. Paul, making him a significant author of the New Testament. The Pauline letters were written to churches and individuals addressing various issues in the early church and offering instructions and encouragement to the believers of his day. To understand Paul’s message as a whole, we clustered his letters, their chapters, and their verses respectively to identify the topics that are addressed repeatedly or once, offering different views at three granularities with each complementing the other. Our calculation implied all Pauline letters could be divided into six groups, well in line with the church tradition. They were First and Second Thessalonians, First and Second Corinthians, Colossians and Ephesians, Galatians and Romans, Philemon and Philippians, and First and Second Timothy and Titus. Among all letters First Corinthians had the most unique chapters, while First Corinthians and Romans had the most unique verses. Furthermore, similar verses and chapters from his letters were also discovered. We also studied the authorship of the letter to Hebrews, an anonymous letter in the New Testament. Using known Pauline and non-Pauline letters as training data, we built nine highly reliable predictors, which collectively predicted that the book of Hebrews was not authored by Paul.


Cite this paper
Hu, W. (2013). Study of Pauline Epistles in the New Testament Using Machine Learning. Sociology Mind, 3, 193-203. doi: 10.4236/sm.2013.32026.
References
[1]   Blei, D., Ng, A., & Jordan, M. (2003) Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.

[2]   Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. Science, 315, 972-976. doi:10.1126/science.1136800

[3]   Goodspeed, E. J. (1933) The meaning of Ephesians. Chicago: University of Chicago Press.

[4]   Griffiths, T., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101, 5228-5235. doi:10.1073/pnas.0307752101

[5]   Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. T. (2007). Topics in semantic representation. Psychological Review, 114, 211-244. doi:10.1037/0033-295X.114.2.211

[6]   Kostenberger, A. J., & Wilder, T. L. (2010) Entrusted with the Gospel: Paul’s theology in the pastoral epistles. Nashville, TN: B&H Publishing Group.

[7]   O’Brien, P. T. (1999) The letter to the Ephesians (pillar new testament commentary). Grand Rapids, MI: Wm. B. Eerdmans Publishing Company.

[8]   Steyvers, M., & Griffiths, T. (2007). Probabilistic topic models. In T. Landauer, D. McNamara, S. Dennis, & W. Kintsch (Eds.), Latent semantic analysis: A road to meaning. Hillsdale, NJ: Laurence Erlbaum.

[9]   Stott, J. (2001) The message of Romans: God’s good news for the world. Downers Grove, IL: IVP Academic.

 
 
Top