%0 Journal Article
%T Multiple Communication Channels in Literary Texts
%A Emilio Matricciani
%J Open Journal of Statistics
%P 486-520
%@ 2161-7198
%D 2022
%I Scientific Research Publishing
%R 10.4236/ojs.2022.124030
%X <span style=\"font-family:Verdana;\">The statistical theory of
language translation is used to compare how a literary character speaks to
different audiences by diversifying two important linguistic communication
channels: the ¡°sentences channel¡± and the ¡°interpunctions channel¡±. The theory
can ¡°measure¡± how the author shapes a character speaking to different
audiences, by modulating deep-language parameters. To show its power, we have
applied the theory to the literary corpus of Maria Valtorta, an Italian mystic
of the XX-century. The likeness index </span><span><span style=\"font-family:Verdana;\">, ranging from 0 to 1, allows to ¡°measure¡± how two linguistic
channels are similar, therefore implying that a character speaks to different
audiences in the </span><span><span style=\"font-family:Verdana;\">same way. A 6-dB difference
between the signal-to-noise ratios of two channels already gives I</span><sub><span style=\"font-family:Verdana;\">L</span></sub><span style=\"font-family:Verdana;\"> ¡Ö 0.5, a threshold below which the two channels depend</span></span><span style=\"font-family:Verdana;\"> very
little on each other, therefore implying that the character addresses different
audiences differently. In conclusion, multiple linguistic channels can describe
the ¡°fine tuning¡± that a literary author uses to diversify characters or
distinguish the behavior of the same character in different situations. The
theory can be applied to literary corpora written in any alphabetical language.</span></span>
%K Alphabetical Language
%K Communication Channels
%K Information
%K Likeness In-dex
%K Literary Character
%K Literary Text
%K Maria Valtorta
%K Signal-to-Noise Ratio
%K Symmetry Index
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=119156