Multiple Communication Channels in Literary Texts ()
ABSTRACT
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 , 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 same way. A 6-dB difference
between the signal-to-noise ratios of two channels already gives IL ≈ 0.5, a threshold below which the two channels depend 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.
Share and Cite:
Matricciani, E. (2022) Multiple Communication Channels in Literary Texts.
Open Journal of Statistics,
12, 486-520. doi:
10.4236/ojs.2022.124030.