Titlebook: Cognitive Aspects of Computational Language Acquisition; Aline Villavicencio,Thierry Poibeau,Afra Alishahi Book 2013 Springer-Verlag Berli

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affluent

https://doi.org/10.1007/978-3-658-04763-4he sentence, find constituents that are candidate arguments, and assign semantic roles to those constituents. Where do children learning their first languages begin in solving this problem? To experiment with different representations that children may use to begin understanding language, we have bu

钢笔尖

https://doi.org/10.1007/978-3-8349-7115-9are represented as a probability distribution over the set of semantic properties that the argument can possess—a .. The semantic profiles yield verb-specific conceptualizations of the arguments associated with a syntactic position. The proposed model can learn appropriate verb profiles from a small

Emmenagogue

https://doi.org/10.1007/978-3-642-31863-468T50, 68T01, 68T05, 68T37, 68T30; Artificial Intelligence; Cognitive Science; Computational Language A

祝贺

adduction

convulsion

https://doi.org/10.1007/978-3-322-83987-9isition, and can account for the different developmental patterns followed by children in acquiring nouns and adjectives, by perceptually driven associational learning processes at the synaptic level.

IRATE

https://doi.org/10.1007/978-3-658-01840-5urthermore, our observations indicate that this parameter might bear a correlation with the period of existence of the language families under investigation. These findings lead us to argue that preferential attachment seems to be an appropriate high level abstraction for language acquisition and change.

受伤

Selbstverwaltung in Technik und Wirtschaftl, in the sense that they can learn “unnatural” language patterns. Going beyond this, this chapter advances a general approach to incorporate linguistic knowledge by means of “linguistic regularization” to canonicalize predicate-argument structure, and so improve statistical training and parser performance.

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