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Assaf, D., Newman, Y., Choen, Y., Argamon, S., Howard, N., Last, M., Frieder, O. and Koppel, M. (2013) Why “Dark Thoughts” Aren’t Really Dark: A Novel Algorithm for Metaphor Identification. Proceedings of the 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Singapore, 16-19 April 2013, 60-65.
https://doi.org/10.1109/CCMB.2013.6609166

has been cited by the following article:

• TITLE: Multiple Communication Channels in Literary Texts

  AUTHORS: Emilio Matricciani

  KEYWORDS: Alphabetical Language, Communication Channels, Information, Likeness In-dex, Literary Character, Literary Text, Maria Valtorta, Signal-to-Noise Ratio, Symmetry Index

  JOURNAL NAME: Open Journal of Statistics, Vol.12 No.4, August 15, 2022

  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.