Clustering Biblical Texts Using Recurrent Neural Networks

This study examines linguistic variation within Biblical Hebrew
by using Recurrent Neural Networks (RNNs) to detect differences
and cluster the Old Testament books accordingly. Various linguistic
features are analysed that are traditionally considered to be of importance in analysing linguistic variation. The traditional division
of books as either Early Biblical Hebrew or Late Biblical Hebrew is
hereby put to the test. Results show that RNNs are a fitting method
for analysing the (morpho)syntax of a language. The model works
well on both separate features, as well as all the features combined.
On the basis of the results the RNNs provide, we propose that
the diachronic approach to Biblical Hebrew is indeed plausible.
The clusters generally hint to the scholarly division made in the
diachronic approach to linguistic variation