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| Main Authors: | , , |
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| Format: | Preprint |
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2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.09710 |
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| _version_ | 1866913232068804608 |
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| author | Tannor, Shlomo Dershowitz, Nachum Lavee, Moshe |
| author_facet | Tannor, Shlomo Dershowitz, Nachum Lavee, Moshe |
| contents | Midrash collections are complex rabbinic works that consist of text in multiple languages, which evolved through long processes of unstable oral and written transmission. Determining the origin of a given passage in such a compilation is not always straightforward and is often a matter of dispute among 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 recent advances in natural language processing for Hebrew texts. Additionally, we demonstrate how this method can be applied to uncover lost material from a specific midrash genre, Tan\d{h}uma-Yelammedenu, that has been preserved in later anthologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_09710 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Style Classification of Rabbinic Literature for Detection of Lost Midrash Tanhuma Material Tannor, Shlomo Dershowitz, Nachum Lavee, Moshe Computation and Language Machine Learning Midrash collections are complex rabbinic works that consist of text in multiple languages, which evolved through long processes of unstable oral and written transmission. Determining the origin of a given passage in such a compilation is not always straightforward and is often a matter of dispute among 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 recent advances in natural language processing for Hebrew texts. Additionally, we demonstrate how this method can be applied to uncover lost material from a specific midrash genre, Tan\d{h}uma-Yelammedenu, that has been preserved in later anthologies. |
| title | Style Classification of Rabbinic Literature for Detection of Lost Midrash Tanhuma Material |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2211.09710 |