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Correction ARTICLE

Front. Hum. Neurosci., 30 January 2018 | https://doi.org/10.3389/fnhum.2018.00012

Corrigendum: Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective

  • 1Department of Experimental and Neurocognitive Psychology, Freie Universität Berlin, Berlin, Germany
  • 2Dahlem Institute for Neuroimaging of Emotion, Berlin, Germany
  • 3Center for Cognitive Neuroscience Berlin, Berlin, Germany

A corrigendum on
Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective

by Jacobs, A. M. (2017). Front. Hum. Neurosci. 11:622. doi: 10.3389/fnhum.2017.00622

In the original article, Equation (1) in Appendix B in Data Sheet 1 contains an error. The correct equation is:

(1) mean[GNsim(word, label_1pos) + … + GNsim(word, label_Npos)] − mean[GNsim(word, label_1neg) + … + GNsim(word, label_Nneg)]

where GNsim is the so-called Lin similarity (Lin, 1998) defining semantic relatedness via a formula derived from information theory. This measure is sometimes called a universal semantic similarity measure as it is supposed to be application-, domain-, and resource independent (cf. Budanitsky and Hirst, 2006).

label_1pos and label_1neg/label_Npos and label_Nneg are the first and last terms, respectively, in either the valence or AP lists given in S2 and S3 of the supplementary materials, i.e., BEFRIEDIGUNG (satisfaction), ANGST (fear), or VERGNÜGEN (have fun), TRAUERN (mourn), and ANMUT (grace), WONNE (delight), or ABSCHEU (abomination), ZUMUTUNG (impertinence).

The original file Data Sheet 1 in the Supplementary Material has been updated.

Conflict of Interest Statement

The author declares that the rvesearch was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Budanitsky, A., and Hirst, G. (2006). Evaluating wordnet-based measures of lexical semantic relatedness. Comput. Linguist. 32, 13–47. doi: 10.1162/coli.2006.32.1.13

CrossRef Full Text | Google Scholar

Lin, D. (1998). “An information-theoretic definition of similarity,” in Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98) (Madison, WI), 296–304.

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Keywords: neurocognitive poetics, quantitative narrative analysis, machine learning, digital humanities, neuroaesthetics, computational stylistics, literary reading, decision trees

Citation: Jacobs AM (2018) Corrigendum: Quantifying the Beauty of Words: A Neurocognitive Poetics Perspective. Front. Hum. Neurosci. 12:12. doi: 10.3389/fnhum.2018.00012

Received: 03 January 2018; Accepted: 11 January 2018;
Published: 30 January 2018.

Edited and reviewed by: Xiaolin Zhou, Peking University, China

Copyright © 2018 Jacobs. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Arthur M. Jacobs, ajacobs@zedat.fu-berlin.de