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Mathematics and Poetry • Unification, Unity, Union
Article

Statistics and Machine Learning Experiments in English and Romanian Poetry

Department of Mathematics & Statistics, Eastern Michigan University, Ypsilanti, MI 48197, USA
Received: 10 June 2020 / Revised: 28 June 2020 / Accepted: 28 June 2020 / Published: 11 December 2020
(This article belongs to the Special Issue Mathematics and Poetry, with a View towards Machine Learning)
This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry. View Full-Text
Keywords: entropy; Kullback–Leibler relative entropy; recurrent neural networks; learning entropy; Kullback–Leibler relative entropy; recurrent neural networks; learning
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MDPI and ACS Style

Calin, O. Statistics and Machine Learning Experiments in English and Romanian Poetry. Sci 2020, 2, 92. https://doi.org/10.3390/sci2040092

AMA Style

Calin O. Statistics and Machine Learning Experiments in English and Romanian Poetry. Sci. 2020; 2(4):92. https://doi.org/10.3390/sci2040092

Chicago/Turabian Style

Calin, Ovidiu. 2020. "Statistics and Machine Learning Experiments in English and Romanian Poetry" Sci 2, no. 4: 92. https://doi.org/10.3390/sci2040092

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