Clustering Biblical Texts Using Recurrent Neural Networks

What it is about

The authors test the Early vs. Late Biblical Hebrew division (Genesis–Kings vs. Chronicles, Daniel, Ecclesiastes, Esther, Ezra–Nehemiah, Song of Songs) with LSTM language models trained on ETCBC data. Besides the words themselves they train on four annotation streams: verbal stem (binyan), clause type, phrase function and part of speech. For each book they average the hidden state over sampled passages and cluster the books with Ward's method. The verbal-stem stream separates the two groups most cleanly; the t-SNE plots agree with the literature that Hif'il is commoner early and Pi'el late. Song of Songs groups with Exodus and Leviticus, which the authors suspect is because all three are dominated by quoted speech. Their reading: the results lend some support to a diachronic division, with a middle group of books that moves between clusters.

Ideas

• I1. A transparent version without a neural net: per book (and per parasha) count the share of each binyan, of wayyiqtol clauses, and of each POS; cluster the five Torah books on these vectors and print the features that separate them.
• I2. Control for speech: compute the same profiles separately for narrator and character voice (the prag voice column), since the paper's Song of Songs anomaly suggests that quoted speech, not period, can drive the clusters.
• I3. Use Pi'el/Hif'il/Nif'al ratios as one of the stage markers in the comparison chapter, with Torah counts as the classical baseline.

For Mova

I1 and I3 are counts for 12. Biblical, Mishnaic and Modern Hebrew compared and 06-verb.md (binyanim) from the macula trees' FEATS; I2 uses tools/macula/src/prag.rs output to split by voice. All deterministic, so two runs must give identical tables.

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.5281/zenodo.4003508

@inproceedings{schans2020clustering,
  title = {{Clustering Biblical Texts Using Recurrent Neural Networks}},
  author = {Schans, Yanniek van der and Ruhe, David and Peursen, Wido van and Bhulai, Sandjai},
  booktitle = {Zenodo (CERN European Organization for Nuclear Research)},
  year = {2020},
  doi = {10.5281/zenodo.4003508},
  url = {https://doi.org/10.5281/zenodo.4003508}
}

References

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