A computational model for the item-based induction of construction networks

Gaspers J, Cimiano P (2014)
Cognitive Science 38(3): 439-488.

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Abstract
According to usage-based approaches to language acquisition, linguistic knowledge is represented in the form of constructions – form-meaning pairings – at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically-specific and item-based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous context. In order to determine meanings of constructions starting from ambiguous contexts we rely on the principle of cross-situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model we show how cross-situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences.
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Gaspers J, Cimiano P. A computational model for the item-based induction of construction networks. Cognitive Science. 2014;38(3):439-488.
Gaspers, J., & Cimiano, P. (2014). A computational model for the item-based induction of construction networks. Cognitive Science, 38(3), 439-488.
Gaspers, J., and Cimiano, P. (2014). A computational model for the item-based induction of construction networks. Cognitive Science 38, 439-488.
Gaspers, J., & Cimiano, P., 2014. A computational model for the item-based induction of construction networks. Cognitive Science, 38(3), p 439-488.
J. Gaspers and P. Cimiano, “A computational model for the item-based induction of construction networks”, Cognitive Science, vol. 38, 2014, pp. 439-488.
Gaspers, J., Cimiano, P.: A computational model for the item-based induction of construction networks. Cognitive Science. 38, 439-488 (2014).
Gaspers, Judith, and Cimiano, Philipp. “A computational model for the item-based induction of construction networks”. Cognitive Science 38.3 (2014): 439-488.
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70 References

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From syllable to syntax: Multilevel statistical learning by 12-month-old infants
Saffran, Infancy 4(2), 2003
Statistical learning by 8-month-old infants.
Saffran JR, Aslin RN, Newport EL., Science 274(5294), 1996
PMID: 8943209

Schatten, 2003
Cross-situational learning: An experimental study of word-learning mechanisms
Smith, Cognitive Science 35(3), 2011
Unsupervised learning of natural languages.
Solan Z, Horn D, Ruppin E, Edelman S., Proc. Natl. Acad. Sci. U.S.A. 102(33), 2005
PMID: 16087885

Tomasello, 1992
First steps toward a usage-based theory of language acquisition
Tomasello, Cognitive Linguistics 11(1-2), 2000
The item-based nature of children's early syntactic development.
Tomasello M., Trends Cogn. Sci. (Regul. Ed.) 4(4), 2000
PMID: 10740280

Tomasello, 2003
Differential productivity in young children's use of nouns and verbs.
Tomasello M, Akhtar N, Dodson K, Rekau L., J Child Lang 24(2), 1997
PMID: 9308423
Social symbol grounding and language evolution
Vogt, Interaction Studies 8(1), 2007
An empirical generative framework for computational modeling of language acquisition.
Waterfall HR, Sandbank B, Onnis L, Edelman S., J Child Lang 37(3), 2010
PMID: 20420744

Wong, 2006
The emergence of links between lexical acquisition and object categorization: A computational study
Yu, Connection Science 17(3-4), 2005

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