Sound symbolism in Japanese names: Machine learning approaches to gender classification.

Autor: Ngai CH; East Asian Languages and Cultures Department, Indiana University, Bloomington, Indiana, United States of America., Kilpatrick AJ; Faculty of International Studies, Nagoya University of Business and Commerce, Nisshin, Aichi, Japan., Ćwiek A; Leibniz-Centre General Linguistics, Laboratory Phonology, Berlin, Germany.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Mar 11; Vol. 19 (3), pp. e0297440. Date of Electronic Publication: 2024 Mar 11 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0297440
Abstrakt: This study investigates the sound symbolic expressions of gender in Japanese names with machine learning algorithms. The main goal of this study is to explore how gender is expressed in the phonemes that make up Japanese names and whether systematic sound-meaning mappings, observed in Indo-European languages, extend to Japanese. In addition to this, this study compares the performance of machine learning algorithms. Random Forest and XGBoost algorithms are trained using the sounds of names and the typical gender of the referents as the dependent variable. Each algorithm is cross-validated using k-fold cross-validation (28 folds) and tested on samples not included in the training cycle. Both algorithms are shown to be reasonably accurate at classifying names into gender categories; however, the XGBoost model performs significantly better than the Random Forest algorithm. Feature importance scores reveal that certain sounds carry gender information. Namely, the voiced bilabial nasal /m/ and voiceless velar consonant /k/ were associated with femininity, and the high front vowel /i/ were associated with masculinity. The association observed for /i/ and /k/ stand contrary to typical patterns found in other languages, suggesting that Japanese is unique in the sound symbolic expression of gender. This study highlights the importance of considering cultural and linguistic nuances in sound symbolism research and underscores the advantage of XGBoost in capturing complex relationships within the data for improved classification accuracy. These findings contribute to the understanding of sound symbolism and gender associations in language.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Ngai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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