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Acquisition of aspect in self-organizing connectionist models

Bibliographic Data

ID3989592
AuthorsXiaowei Zhao (0000-0002-2313-8083, University of Richmond), Ping Li (0000-0002-0813-0980)
Year2009
Volume47
Issue5
Publication date2009-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueLinguistics (JOURNAL)
Journal identifiersISSN: 0024-3949 • E-ISSN: 1613-396X
PublisherWalter de Gruyter GmbH (PUBLISHER • DE)
DOI10.1515/ling.2009.038
OpenAlexW2167433478
LanguageEN
Citations received2
References cited33

Two connectionist networks, DISLEX and DevLex-II, were used in this study to model the acquisition of lexical and grammatical aspect. Both models use multi-layered self-organizing feature maps, connected by associative links trained according to the Hebbian learning rule. Previous empirical research has identified a strong association between lexical aspect and grammatical aspect in child language, on the basis of which some researchers argue for innate semantic categories or prelinguistic predispositions. Our simulations indicate that such an association can emerge from dynamic self-organization and Hebbian learning in connectionist networks, without the need of a priori assumptions about the structure of innate knowledge. Our modeling results further attest to the utility of self-organizing neural networks in the study of language acquisition

Artificial neural network · Association (psychology) · Associative learning · Associative property · Cognitive psychology · Cognitive science · Connectionism · Feature (linguistics) · Hebbian theory · Language acquisition · Leabra · Linguistics · Natural language processing · Self-organizing map · Semantic network · Artificial Intelligence · Computer Science · Language and cultural evolution · Mathematics · Natural Language Processing Techniques · Psychology · Speech and dialogue systems

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Unique citing works2
Citations per year0,13
Citation span2010 - 2023 (14)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 2

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