On the semantics of nonwords and their lexical categories

Giovanni Cassani*, Yu-Ying Chuang, R. Harald Baayen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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 phonolog- ically 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.
Original languageEnglish
JournalJournal of Experimental Psychology-Learning Memory and Cognition
Early online date2019
DOIs
Publication statusPublished - 2019
Externally publishedYes

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Semantics
Discriminant analysis
Acoustic waves

Cite this

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title = "On the semantics of nonwords and their lexical categories",
abstract = "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 phonolog- ically 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.",
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On the semantics of nonwords and their lexical categories. / Cassani, Giovanni; Chuang, Yu-Ying; Baayen, R. Harald.

In: Journal of Experimental Psychology-Learning Memory and Cognition, 2019.

Research output: Contribution to journalArticleScientificpeer-review

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