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Mapping Topic Evolution Across Poetic Traditions
arXiv - CS - Computation and Language Pub Date : 2020-06-28 , DOI: arxiv-2006.15732
Petr Plechac, Thomas N. Haider

Poetic traditions across languages evolved differently, but we find that certain semantic topics occur in several of them, albeit sometimes with temporal delay, or with diverging trajectories over time. We apply Latent Dirichlet Allocation (LDA) to poetry corpora of four languages, i.e. German (52k poems), English (85k poems), Russian (18k poems), and Czech (80k poems). We align and interpret salient topics, their trend over time (1600--1925 A.D.), showing similarities and disparities across poetic traditions with a few select topics, and use their trajectories over time to pinpoint specific literary epochs.

中文翻译:

映射跨诗意传统的主题演变

不同语言的诗歌传统以不同的方式演变,但我们发现某些语义主题出现在其中的几个中,尽管有时会出现时间延迟,或者随着时间的推移出现不同的轨迹。我们将潜在狄利克雷分配(LDA)应用于四种语言的诗歌语料库,即德语(52k 首诗)、英语(85k 首诗)、俄语(18k 首诗)和捷克语(80k 首诗)。我们对齐和解释突出的主题,它们随着时间的推移(公元 1600--1925 年)的趋势,展示了诗歌传统与一些选定主题的相似性和差异性,并使用它们随时间推移的轨迹来确定特定的文学时代。
更新日期:2020-08-31
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