What do Asian and non-Asian scriptures have in common? An applied statistical machine learning inquiry
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This paper presents a substantially detailed statistical machine learningapproach to the analysis of several aspects of sacred texts from both the Asian andBiblical scriptural canons. The corpus herein considered consists of 4 Asian sacredscriptures, namely the Tao Te Ching, the teachings of the Buddha, the Yogasutras ofPatanjali, and the Upanishads, and 4 non-Asian sacred texts essentially four booksfrom the Bible, namely the Book of Proverbs, the Book of Wisdom, the Book ofEcclesiastes and the Book of Ecclesiasticus. Standard text mining tools are used,like the creation of Document Term Matrices (DTM) to pre-process raw Englishtranslations into word frequencies, and both unsupervised and supervised learningmethods are used to answer some foundational questions featuring similarities anddissimilarities within each canon and interesting differences between all the canonsconsidered. Despite the vast disparities between the translators of the originaltexts, our findings reveal sharp differences between Asian and non Asian scripturesregardless of whether clustering techniques or pattern recognition methods are used.We provide several compelling visualizations to help highlight our striking findings,chief of which are the persistent groupings of the scriptures based on geography.
Document typePeer reviewed
Document versionFinal PDF
SourceMathematics for Applications. 2019 vol. 8, č. 2, s. 151-171. ISSN 1805-3629
- 2019/2