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Publication . Article . 2020

On the Semantics of Nonwords and Their Lexical Category

Giovanni Cassani; Yu-Ying Chuang; R. Harald Baayen;
Open Access
Published: 01 Apr 2020 Journal: Journal of Experimental Psychology: Learning, Memory, and Cognition, volume 46, issue 4, pages 621-637 (issn: 0278-7393, Copyright policy )

Using computational simulations, this work demonstrates that it is possible to learn a systematic relation between words' sound and their meanings. The sound-meaning relation was learned from a corpus of phonologically transcribed child-directed speech by using the linear discriminative learning (LDL) framework (Baayen, Chuang, Shafaei-Bajestan, & Blevins, 2019), which implements linear mappings between words' form vectors and semantic vectors. Presented with the form vectors of 16 nonwords, taken from a study on word learning (Fitneva, Christiansen, & Monaghan, 2009), the network generated the estimated semantic vectors of the nonwords. As half of these nonwords were created to phonologically resemble English nouns and the other half were phonologically similar to English verbs, we assessed whether the estimated semantic vectors for these nonwords reflect this word category difference. In 7 different simulations, linear discriminant analysis (LDA) successfully discriminated between noun-like nonwords and verb-like nonwords, based on their semantic relation to the words in the lexicon. Furthermore, how well LDA categorized a nonword correlated well with a phonological typicality measure (i.e., the degree of its form being noun-like or verb-like) and with children's performance in an entity/action discrimination task. On the one hand, the results suggest that children can infer the implicit meaning of a word directly from its sound. On the other hand, this study shows that nonwords do land in semantic space, such that children can capitalize on their semantic relations with other elements in the lexicon to decide whether a nonword is more likely to denote an entity or an action. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Subjects by Vocabulary

Microsoft Academic Graph classification: Mental lexicon Lexicon Categorization English verbs Part of speech Artificial intelligence business.industry business Noun Natural language processing computer.software_genre computer Phonology Psychology Semantics


nonwords, semantics, lexical categories, linear discriminative learning, phonological bootstrapping, SOUND-SHAPE CORRESPONDENCES, SYMBOLISM, ACQUISITION, SIMILARITY, CATEGORIZATION, ARBITRARINESS, BIASES, AGE, Psychology, Linguistics and Language, Language and Linguistics, Experimental and Cognitive Psychology

Related Organizations
Funded by
Wide Incremental learning with Discrimination nEtworks
  • Funder: European Commission (EC)
  • Project Code: 742545
  • Funding stream: H2020 | ERC | ERC-ADG
Validated by funder
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Digital Humanities and Cultural Heritage