Accounting for the stochastic nature of sound symbolism using Maximum Entropy model

Shigeto Kawahara, Hironori Katsuda, Gakuji Kumagai

Research output: Contribution to journalArticle

Abstract

Sound symbolism refers to stochastic and systematic associations between sounds and meanings. Sound symbolism has not received much serious attention in the generative phonology literature, perhaps because most if not all sound symbolic patterns are probabilistic. Building on the recent proposal to analyze sound symbolic patterns within a formal phonological framework (Alderete and Kochetov 2017), this paper shows that MaxEnt grammars allow us to model stochastic sound symbolic patterns in a very natural way. The analyses presented in the paper show that sound symbolic relationships can be modeled in the same way that we model phonological patterns. We suggest that there is nothing fundamental that prohibits formal phonologists from analyzing sound symbolic patterns, and that studying sound symbolism using a formal framework may open up a new, interesting research domain. The current study also reports two hitherto unnoticed cases of sound symbolism, thereby expanding the empirical scope of sound symbolic patterns in natural languages.

Original languageEnglish
Pages (from-to)109-120
Number of pages12
JournalOpen Linguistics
Volume5
Issue number1
DOIs
Publication statusPublished - 2019 Jan 1

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symbolism
entropy
phonology
grammar
Maximum Entropy
Sound
Sound Symbolism
language

Keywords

  • Japanese
  • MaxEnt grammar
  • sound symbolism

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Accounting for the stochastic nature of sound symbolism using Maximum Entropy model. / Kawahara, Shigeto; Katsuda, Hironori; Kumagai, Gakuji.

In: Open Linguistics, Vol. 5, No. 1, 01.01.2019, p. 109-120.

Research output: Contribution to journalArticle

Kawahara, Shigeto ; Katsuda, Hironori ; Kumagai, Gakuji. / Accounting for the stochastic nature of sound symbolism using Maximum Entropy model. In: Open Linguistics. 2019 ; Vol. 5, No. 1. pp. 109-120.
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