Style Classification of Rabbinic Literature for Detection of Lost Midrash Tanhuma Material
What it is about
The authors classify rabbinic passages by genre (Mishnah, Midrash Halakhah, Jerusalem and Babylonian Talmud, Midrash Aggadah, Tanhuma-Yelammedenu) using 50-word blocks from Sefaria. A logistic regression over unigram to trigram counts reaches 0.867 validation accuracy, close to AlephBERT (0.879) and below BEREL (0.922); a morphology-only model reaches 0.560, against a chance rate of 0.166. The baseline is kept because its weights are readable. The classifier is then run on Yalkut Shimoni, and passages with a low score on text reuse (RWFS, 3-gram fuzzy matching, Levenshtein up to 2) are kept as candidate lost Tanhuma. Of 50 sampled candidates, a midrash expert confirmed 30 (22 cited as Yelammedenu and 8 typical Tanhuma material), about 60% precision, and precision rises with the threshold.
Ideas
- I1. Use the logistic-regression weights as the explanation for a genre label: list the n-grams that pushed a passage to a class, which fits our rule that every decision says why.
- I2. Keep the morphology-only model as a control: a feature set that drops the lexicon should lose accuracy; if it does not, the style signal is not what we think.
- I3. Two-stage detection: a style score filtered by a text-reuse check (keep passages with few shared n-grams with known sources). The reuse check can be a deterministic index, with the threshold fixed beforehand.
- I4. Sample-based precision: label a random 50 of the flagged items by hand, as they did, and report the confirmed fraction with the date.
For Mova
I1 and I2 apply to the Mishnaic and rabbinic chapter, 11. Mishnaic Hebrew (hbo-x-mishnaic), and the lexicon notes in The lexicon. I3 would be a new offline tool beside tools/langseg, not part of the parse. The Sefaria data is open in its licence as given; the classifier itself is not a Mova component yet.
Cite
@inproceedings{tannor2022style,
title = {{Style Classification of Rabbinic Literature for Detection of Lost Midrash Tanhuma Material}},
author = {Tannor, Solomon and Dershowitz, Nachum and Lavee, Moshe},
booktitle = {Proceedings of the 2nd International Workshop on Natural Language Processing for Digital Humanities},
year = {2022},
doi = {10.18653/v1/2022.nlp4dh-1.6},
url = {https://doi.org/10.18653/v1/2022.nlp4dh-1.6}
}References
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