Clustering Biblical Texts Using Re-current Neural Networks

Wido van Peursen, Sandjai Bhulai, Yanniek van der Schans, David Ruhe

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the Network Institute Academy Assistants program 2018/2019
EditorsVictor de Boer
Number of pages7
Publication statusPublished - 2020


  • Neural Networks
  • Hebrew Bible
  • Digital Humanities


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