Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers
We introduce novel Information Theory quantifiers in a computational linguistic study that involves a large corpus of English Renaissance literature. The 185 texts studied (136 plays and 49 poems in total), with first editions that range from 1580 to 1640, form a representative set of its period. Ou...
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2009
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v388_n6_p916_Rosso http://hdl.handle.net/20.500.12110/paper_03784371_v388_n6_p916_Rosso |
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paper:paper_03784371_v388_n6_p916_Rosso2023-06-08T15:40:12Z Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers English literature Entropy Information Theory Statistical complexity Computational linguistics Distribution functions Entropy Information theory Probability distributions Data sets English literature Entropy variations Probability distribution functions Statistical complexity Computational complexity We introduce novel Information Theory quantifiers in a computational linguistic study that involves a large corpus of English Renaissance literature. The 185 texts studied (136 plays and 49 poems in total), with first editions that range from 1580 to 1640, form a representative set of its period. Our data set includes 30 texts unquestionably attributed to Shakespeare; in addition we also included A Lover's Complaint, a poem which generally appears in Shakespeare collected editions but whose authorship is currently in dispute. Our statistical complexity quantifiers combine the power of Jensen-Shannon's divergence with the entropy variations as computed from a probability distribution function of the observed word use frequencies. Our results show, among other things, that for a given entropy poems display higher complexity than plays, that Shakespeare's work falls into two distinct clusters in entropy, and that his work is remarkable for its homogeneity and for its closeness to overall means. © 2008 Elsevier B.V. All rights reserved. 2009 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v388_n6_p916_Rosso http://hdl.handle.net/20.500.12110/paper_03784371_v388_n6_p916_Rosso |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
English literature Entropy Information Theory Statistical complexity Computational linguistics Distribution functions Entropy Information theory Probability distributions Data sets English literature Entropy variations Probability distribution functions Statistical complexity Computational complexity |
| spellingShingle |
English literature Entropy Information Theory Statistical complexity Computational linguistics Distribution functions Entropy Information theory Probability distributions Data sets English literature Entropy variations Probability distribution functions Statistical complexity Computational complexity Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers |
| topic_facet |
English literature Entropy Information Theory Statistical complexity Computational linguistics Distribution functions Entropy Information theory Probability distributions Data sets English literature Entropy variations Probability distribution functions Statistical complexity Computational complexity |
| description |
We introduce novel Information Theory quantifiers in a computational linguistic study that involves a large corpus of English Renaissance literature. The 185 texts studied (136 plays and 49 poems in total), with first editions that range from 1580 to 1640, form a representative set of its period. Our data set includes 30 texts unquestionably attributed to Shakespeare; in addition we also included A Lover's Complaint, a poem which generally appears in Shakespeare collected editions but whose authorship is currently in dispute. Our statistical complexity quantifiers combine the power of Jensen-Shannon's divergence with the entropy variations as computed from a probability distribution function of the observed word use frequencies. Our results show, among other things, that for a given entropy poems display higher complexity than plays, that Shakespeare's work falls into two distinct clusters in entropy, and that his work is remarkable for its homogeneity and for its closeness to overall means. © 2008 Elsevier B.V. All rights reserved. |
| title |
Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers |
| title_short |
Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers |
| title_full |
Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers |
| title_fullStr |
Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers |
| title_full_unstemmed |
Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers |
| title_sort |
shakespeare and other english renaissance authors as characterized by information theory complexity quantifiers |
| publishDate |
2009 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03784371_v388_n6_p916_Rosso http://hdl.handle.net/20.500.12110/paper_03784371_v388_n6_p916_Rosso |
| _version_ |
1768546255183544320 |