Identification of Parallel Passages Across a Large Hebrew/Aramaic Corpus
What it is about
A fast method for finding all approximate parallel passages (20+ words) in a large Hebrew/Aramaic corpus, where spelling (plene vs defective, final letters, א/י), particles and synonyms vary. Each word is reduced to its two rarest letters (by corpus letter frequency), keeping their order; this strips most prefixes and matres lectionis, so דילמא and דלמא both become דל. From every five-word window four "skip-grams" of four words are taken (each leaving one word out), packed into collision-free integers and indexed. A parallel is a cluster of at least 3 matching skip-grams, gaps of at most 8 words, spanning at least 20 words, found by binning matches along the diagonal of a dot-plot. On the Babylonian Talmud (1.8 million words) it finds 4 602 parallel pairs in about 11 seconds on one thread; on a test pair of tractates it finds all 41 parallels that exhaustive Levenshtein finds, plus 5 more, with no false positives seen.
Ideas
- I1. The two-rarest-letters key is a cheap spelling-robust word signature for unvocalised Hebrew: use it to match Torah words against other witnesses (Samaritan Pentateuch, Qumran texts, Mishnah quotations of verses) where plene/defective spelling differs, before any morphological analysis.
- I2. Skip-gram clusters as the parallel finder for quotation detection: find every Torah verse quoted or paraphrased in the Mishnah corpus, giving aligned Biblical–Mishnaic pairs for the comparison chapter.
- I3. The whole method is integer-only and deterministic; a Rust implementation is small and fits Mova's rule of BTreeMap order and two identical runs. Measure recall on known inner-Torah parallels (Exod 20 / Deut 5) against a plain lemma-Jaccard method.
For Mova
I1 is a helper for 1. Script and pointing (matres lectionis, plene/defective) and for any OCR or witness-alignment work. I2 feeds 11-mishnaic.md and 12-biblical-mishnaic-modern.md with aligned quotations. I3 is a tool to write beside tools/macula; the paper is read-only, so the method is reimplemented from its description.
Cite
@article{shmidman2016identification,
title = {{Identification of Parallel Passages Across a Large Hebrew/Aramaic Corpus}},
author = {Shmidman, Avi and Koppel, Moshe and Porat, Ely},
journal = {Journal of Data Mining \& Digital Humanities, Special Issue on Computer-Aided Processing of Intertextuality in Ancient Languages, Towards a Digital Ecosystem: NLP. Corpus infrastructure. Methods for Retrieving Texts and Computing Text Similarities (March 11, 2018) jdmdh:1388},
year = {2016},
doi = {10.46298/jdmdh.1388},
eprint = {1602.08715},
archivePrefix = {arXiv},
url = {https://doi.org/10.46298/jdmdh.1388}
}References
- Basile C., Benedetto D., Caglioti G., and Degli Esposti M. A Plagiarism Detection Procedure in Three Steps: Selection, Matches and Squares, PAN 2009:19-23. find
- Barron-Cedeno A., Basile C., Degli Esposti M. and Rosso P. Word length n-grams for text re-use detection, in Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing, ser. CICLing’10. 2010:687-699. find
- Büchler M. Informationstechnische Aspekte des Historischen Text Re-use (Engl. Computational Aspects of Historical Text Re-use Detection), Ph.D. dissertation, Universitat Leipzig, 2013. find
- Büchler, M., Burns, P. R., Müller, M., Franzini, E., and Franzini, G. Towards a Historical Text Re-use Detection, in: Biemann, C. and Mehler, A. (eds.) Text Mining, Theory and Applications of Natural Language Processing. 2014. ↗
- Skolnik F. (ed.) Encyclopedia Judaica, second edition. Keter Publishing House (Jerusalem), 2007. find
- Franzini G., Büchler M., Franzini E., and Moritz M. Scaling Historical Text Re-use. The IEEE International Conference on Big Data 2014 (IEEE BigData 2014), 2014:27-30. find
- Grozea C., Gehl C., and Popescu M. ENCOPLOT: Pairwise Sequence Matching in Linear Time Applied to Plagiarism Detection. 3rd PAN Workshop. Uncovering Plagiarism, Authorship and Social Software Misuse. 2009:10. find
- Grozea C., and Popescu M. The Encoplot Similarity Measure for Automatic Detection of Plagiarism Notebook for PAN at CLEF 2011. find
- Guthrie D., Allison B., Liu W., Guthrie L. and Wilks Y. A Closer Look at Skip-gram Modelling. Proceedings of the 5th International Conference on Language Resources and Evaluation. 2006:1-4. find
- Harris Z. Distributional structure. Word. 1954;10(23):146-162. http1 https://www.dropbox.com/s/e3syo8vhncptruj/FindAllSimilarPassages.exe ↗
- Karp R. and Rabin M. O. Efficient randomized pattern-matching algorithms. IBM Journal of Research and Development 1987; 31(2):249-260. find
- Klein E., Dershowitz N., Wolf L., Almogi O., and Wangchuk D. Finding Inexact Quotations Within a Tibetan Buddhist Corpus. Digital Humanities. 2014:486-488. find
- Lee, J. A Computational Model of Text Reuse in Ancient Literary Texts. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. 2007:472-479 find
- Lee L. Measures of distributional similarity. 37th Annual Meeting of the Association for Computational Linguistics. 1999:25-32. find
This page in Markdown → https://md.mova.elamur.ai/free/en/docs/papers/hbo-2016-shmidman-identification-parallel-passages-across-large/