Style Classification of Rabbinic Literature for Detection of Lost Midrash Tanhuma Material (2022)

Abstract: Midrash collections are complex rabbinic works that consist of text in multiple languages, that evolved through long processes of instable oral and written transmission. Determining the origin of a given passage in such a compilation is not always straightforward and is often a matter disputed by scholars, yet it is essential for scholars’ understanding of the passage and its relationship to other texts in the rabbinic corpus. To help solve this problem, we propose a system for classification of rabbinic literature based on its style, leveraging recently released pretrained Transformer models for Hebrew. Additionally, we demonstrate how our method can be applied to uncover lost material from the Midrash Tanhuma.

## introduction midrash anthologies are multi-layered works that consist of text in multiple languages, composed by different authors spanning different generations and locations. the midrash collator often merges and quotes various earlier sources, sometimes paraphrasing previous material. these complex processes can make it hard for scholars to clearly separate and detect the different sources which the collection is composed of. identifying sections which originate in one source or another can shed light on many scholarly debates and help researchers gain a better understanding of the historical development of the rabbinic corpus. the ability to analyze and classify rabbinic texts in an automated way has tremendous potential. placing old manuscripts, uncovering lost material that is quoted in later works (e.g. parts of midrash tanh . uma, mekhilta deuteronomy), and determining authorship or dating of a text are examples for such uses. this great potential motivated us to turn to current state-of-the-art natural language processing (nlp) methods to determine whether we can currently solve any such high-impact problem. we propose a system for classification of rabbinic literature by detecting unique stylistic patterns in the language of the text. additionally, we demonstrate how our classifier can be used to uncover lost midrashic material that is quoted in later works. as a test case, we apply our method to detect lost sections of the midrash tanh . uma that are quoted in the yalkut shimoni. foot_0 ## related work work from recent years on authorship attribution and plagiarism detection has demonstrated the effectiveness of stylometry and literary style classification in general. @xcite perform automatic biblical source criticism by looking at preferences among synonyms and other stylistic attributes. siegal and shmidman (2018) used computational tools to help reconstruct the lost mekhilta deuteronomy. they start off with a list of candidate texts, and the main problem they focus on is removing quotes or near-quotes of existing material from other sources. ithaca @xcite is an impressive toolkit for restoration and classification of ancient greek epigraphs. ## results as can be seen in after taking the whole yalkut shimoni on the pentateuch and following the process described in section 3.4, we can analyze the prevalence of each class in the collection. as can be seen in figure 1 , the babylonian talmud is the most quoted class, while the jerusalem talmud is rarely, if ever, quoted. our classifier gives a similar distribution to that of the text-reuse engine. however, when looking only at passages with low reuse score we see that the babylonian talmud rarely appears while 'tanh . uma' becomes the most frequent predicted class by far, followed by 'midrash halakha.' this aligns with the fact that we know of lost works that belong to these categories, while the babylonian talmud was well preserved throughout the generations as the core text of the rabbinic tradition. to evaluate our classifier on the target task, we sampled for manual labeling a random set of 50 items classified as tanh . uma. a midrash expert analyzed these passages and looked them up in the early print edition of yalkut shimoni, which tends to include citations in the margins. sections that were ascribed to yelammedenu ( ‫)ילמדנוּ‬ and sections that were recognized as being typical tanh . uma material were labeled as "positive," while all other passages were labeled "negative." out of these items, 22 were cited as yelammedenu, while an additional 8 were recognized as typical tanh . uma material from lost sources, foot_5 yielding an approximate precision of 60%. from figure 3 , we see that the precision grows monotonically with the decision threshold, indicating that the model is useful in recovering lost tanh . uma material. furthermore, we see that we can achieve a precision of approximately 80% by setting an appropriate decision threshold without a high cost to recall. ## discussion and future work our results for detecting tanh . uma sections in yalkut shimoni demonstrate that our method can be a useful tool for researchers working on recovering lost rabbinic material. we are planning a digital library of tanh . uma-yelammedenu literature and believe our work will be of high value to researchers working on detecting lost material of this genre. we intend to run our classifier on additional collections such as midrash hagadol for which we don't currently have ground truth labels to help uncover additional lost tanh . uma passages. our method can be expanded and applied to many more open questions in jewish studies. an obvious direction involves applying it to other lost midrashic material. another is exploring the baraitot 8 that appear in the babylonian talmud and the jerusalem talmud and their relationship to each other. also promising would be to apply it to the many fragmentary manuscripts that have been found in collections like the cairo geniza. this would require dealing carefully with noisy text with errors originating in handwritten text recognition.