Projects

2025

• Integrating Semantic and Statistical Features for Authorial Clustering of Qumran Scrolls
  Yonatan Lourie, Prof. Roded Sharan, Prof. Jonathan Ben Dov
  Accepted to the ALP Workshop at NAACL 2025
  Paper

  Abstract

  We present a novel framework for authorial classification and clustering of the Qumran Dead Sea Scrolls (DSS). Our approach combines modern Hebrew BERT embeddings with traditional natural language processing features in a graph neural network (GNN) architecture.

  Our results outperform baseline methods on both the Dead Sea Scrolls and a validation dataset of the Hebrew Bible. In particular, we leverage our model to provide significant insights into long-standing debates, including the classification of sectarian and non-sectarian texts and the division of the Hodayot collection of hymns.

  Interactive visualizations are available at the project site.