The Acquisition of Noun and Verb Categories by Bootstrapping From a Few Known Words: A Computational Model.

ABSTRACT: While many studies have shown that toddlers are able to detect syntactic regularities in speech, the learning mechanism allowing them to do this is still largely unclear. In this article, we use computational modeling to assess the plausibility of a context-based learning mechanism for the acquisition of nouns and verbs. We hypothesize that infants can assign basic semantic features, such as "is-an-object" and/or "is-an-action," to the very first words they learn, then use these words, the semantic seed, to ground proto-categories of nouns and verbs. The contexts in which these words occur, would then be exploited to bootstrap the noun and verb categories: unknown words are attributed to the class that has been observed most frequently in the corresponding context. To test our hypot

SUBMITTER: Brusini P 

PROVIDER: S-EPMC8416756 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature