Integrating Semantic and Statistical Features for Authorial Clustering of Qumran Scrolls
What it is about
The paper clusters the Hebrew Dead Sea Scrolls to test two long-standing questions: whether the Hodayot hymns (1QHa) split into "Teacher Hymns" and "Community Hymns", and how well sectarian and non-sectarian texts separate. The Hebrew scrolls, with reconstructed text removed, are cut into 978 chunks of 100 words with 15 words of overlap. Each chunk is described by BEREL and AlephBERT embeddings, and by TF–IDF and character-trigram vectors. A graph links chunks whose combined TF–IDF and trigram cosine similarity passes a threshold, and a two-layer graph autoencoder produces embeddings that are clustered with Ward and k-means. The method beats baselines on the scrolls and on a Hebrew Bible validation set, but the classical TF–IDF and trigram features are about as good as BERT. The Teacher Hymns cluster in columns 10–16, which supports a distinct group. The sectarian split is less clear, and the model groups rewritten Bible texts by genre rather than by sect. The code and a dataset are public.
Ideas
- I1. Use the paper's protocol as a test harness for Hebrew clustering in
tools/hbo: 100-word chunks, 15-word overlap, and Jaccard against the labelled compositions. Report the score with a deterministic baseline before any embedding. - I2. Character trigrams plus TF–IDF as the default feature for forms the lexicon does not know. The paper finds these competitive with BERT on fragmentary text, so the SLM can use them for unknown or damaged words in
tools/ocrfixbefore it reaches for a neural model. - I3. A lexical test of teacher versus community vocabulary: list the words that separate the two hymn groups (the paper's Douglas and Johnson references), and use them as a gold set for a rule-based classifier in
worldthat reads speaker and register.
For Mova
I1 is a test harness for tools/hbo and macula clustering. I2 belongs to tools/ocrfix as the fallback for unknown forms and to The lexicon. I3 is a gold list for world, and a register note for Discourse: narrative, speech and the shape of a text. Before any use, check the licence of the public dataset, which the paper does not state.
Cite
@inproceedings{lourie2025integrating,
title = {{Integrating Semantic and Statistical Features for Authorial Clustering of Qumran Scrolls}},
author = {Lourie, Yonatan and Ben-Dov, Jonathan and Sharan, Roded},
booktitle = {Proceedings of the Second Workshop on Ancient Language Processing},
year = {2025},
doi = {10.18653/v1/2025.alp-1.2},
url = {https://doi.org/10.18653/v1/2025.alp-1.2}
}References
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