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Auceps syllabarum: A Digital Analysis of Latin Prose Rhythm
Journal of Roman Studies ( IF 0.8 ) Pub Date : 2019-09-24 , DOI: 10.1017/s0075435819000881
Tom Keeline , Tyler Kirby

In this article we describe a series of computer algorithms that generate prose rhythm data for any digitised corpus of Latin texts. Using these algorithms, we present prose rhythm data for most major extant Latin prose authors from Cato the Elder through the second centurya.d. Next we offer a new approach to determining the statistical significance of such data. We show that, while only some Latin authors adhere to the Ciceronian rhythmic canon, every Latin author is ‘rhythmical’ — they just choose different rhythms. Then we give answers to some particular questions based on our data and statistical approach, focusing on Cicero, Sallust, Tacitus and Pliny the Younger. In addition to providing comprehensive new data on Latin prose rhythm, presenting new results based on that data and confirming certain long-standing beliefs, we hope to make a contribution to a discussion of digital and statistical methodology in the study of Latin prose rhythm and in Classics more generally. The Supplementary Material available online (https://doi.org/10.1017/S0075435819000881) contains an appendix with tables, data and code. This appendix constitutes a static ‘version of record’ for the data presented in this article, but we expect to continue to update our code and data; updates can be found in the repository of the Classical Language Toolkit (https://github.com/cltk/cltk).

中文翻译:

Auceps syllabarum:拉丁散文节奏的数字分析

在本文中,我们描述了一系列计算机算法,这些算法可以为任何数字化拉丁文本语料库生成散文节奏数据。使用这些算法,我们为大多数现存的主要拉丁散文作者提供散文节奏数据,从卡托长老到二世纪广告. 接下来,我们提供了一种新方法来确定此类数据的统计显着性。我们表明,虽然只有一些拉丁作家遵守西塞罗节奏经典,但每个拉丁作家都是“有节奏的”——他们只是选择了不同的节奏。然后,我们根据我们的数据和统计方法给出一些特定问题的答案,重点关注 Cicero、Sallust、Tacitus 和 Pliny the Younger。除了提供有关拉丁散文节奏的全面新数据,基于该数据呈现新结果并确认某些长期存在的信念外,我们希望为拉丁散文节奏研究中的数字和统计方法的讨论做出贡献经典更普遍。在线提供的补充材料(https://doi.org/10.1017/S0075435819000881) 包含附有表格、数据和代码的附录。本附录构成本文中数据的静态“记录版本”,但我们希望继续更新我们的代码和数据;更新可以在经典语言工具包的存储库中找到(https://github.com/cltk/cltk)。
更新日期:2019-09-24
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