Mapping Topic Evolution Across Poetic Traditions
Computation and Language
2020-08-31 v2 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2006.15732,
title = {Mapping Topic Evolution Across Poetic Traditions},
author = {Petr Plechac and Thomas N. Haider},
journal= {arXiv preprint arXiv:2006.15732},
year = {2020}
}