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https://doi.org/10.31234/osf.i...
Preprint . 2019
License: CC BY
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NARCIS
Article . 2020
Data sources: NARCIS
PsyArXiv
Preprint . 2019
Data sources: PsyArXiv
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On the semantics of non-words and their lexical category

Authors: Cassani, Giovanni; Chuang, Yu-Ying; Baayen, R.;

On the semantics of non-words and their lexical category

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 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).

Countries
Netherlands, Belgium
Related Organizations
Subjects by Vocabulary

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

Keywords

lexical categories, Non-words, phonological bootstrapping, Language and Linguistics, Discrimination Learning, PsyArXiv|Social and Behavioral Sciences|Linguistics|Psycholinguistics and Neurolinguistics, CATEGORIZATION, Psychology, bepress|Social and Behavioral Sciences|Linguistics|Psycholinguistics and Neurolinguistics, semantics, bepress|Social and Behavioral Sciences|Linguistics, Psycholinguistics, SYMBOLISM, SIMILARITY, nonwords, PsyArXiv|Social and Behavioral Sciences|Linguistics, Linguistics and Language, ARBITRARINESS, SOUND-SHAPE CORRESPONDENCES, Experimental and Cognitive Psychology, AGE, Phonetics, Humans, linear discriminative learning, ACQUISITION, BIASES, Models, Theoretical, PsyArXiv|Social and Behavioral Sciences, bepress|Social and Behavioral Sciences

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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