A Usage-Based Model of Early Grammatical Development

Beekhuizen, Barend; Bod, Rens; Fazly, Afsaneh; Stevenson, Suzanne; Verhagen, Arie

doi:10.3115/v1/w14-2006

Cited by 8 publications

(5 citation statements)

References 20 publications

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“…Frank, Goodman, & Tenenbaum, 2009;McMurray, Horst, & Samuelson, 2012;Plunkett, Sinha, Møller, & Strandsby, 1992;Regier, 2005;Siskind, 1996;C. Yu & Ballard, 2007), or learning verb-argument structures assuming nouns and/or syntax are known , 2010Barak, Fazly, & Stevenson, 2013;Beekhuizen, Bod, Fazly, Stevenson, & Verhagen, 2014;Chang, 2008;Morris, Cottrell, & Elman, 2000;S. Niyogi, 2002).…”

Section: Overview Of the Learnermentioning

confidence: 99%

Bootstrapping language acquisition

et al. 2017

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The semantic bootstrapping hypothesis proposes that children acquire their native language through exposure to sentences of the language paired with structured representations of their meaning, whose component substructures can be associated with words and syntactic structures used to express these concepts. The child's task is then to learn a language-specific grammar and lexicon based on (probably contextually ambiguous, possibly somewhat noisy) pairs of sentences and their meaning representations (logical forms). Starting from these assumptions, we develop a Bayesian probabilistic account of semantically bootstrapped first-language acquisition in the child, based on techniques from computational parsing and interpretation of unrestricted text. Our learner jointly models (a) word learning: the mapping between components of the given sentential meaning and lexical words (or phrases) of the language, and (b) syntax learning: the projection of lexical elements onto sentences by universal construction-free syntactic rules. Using an incremental learning algorithm, we apply the model to a dataset of real syntactically complex child-directed utterances and (pseudo) logical forms, the latter including contextually plausible but irrelevant distractors. Taking the Eve section of the CHILDES corpus as input, the model simulates several well-documented phenomena from the developmental literature. In particular, the model exhibits syntactic bootstrapping effects (in which previously learned constructions facilitate the learning of novel words), sudden jumps in learning without explicit parameter setting, acceleration of word-learning (the "vocabulary spurt"), an initial bias favoring the learning of nouns over verbs, and one-shot learning of words and their meanings. The learner thus demonstrates how statistical learning over structured representations can provide a unified account for these seemingly disparate phenomena.

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Section: Overview Of the Learnermentioning

confidence: 99%

Bootstrapping language acquisition

et al. 2017

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“…Se estima, de hecho, que el uso de energía de Ethereum pronto disminuirá en ~99.95% (Beekhuizen, 2021), lo cual supondrá también una ventaja respecto de otras monedas, industrias y aplicaciones tecnológicas cuyo consumo anual de energía es considerablemente mayor (Ver gráfico).…”

Section: Lawrence Girónunclassified

Ethereum y las finanzas descentralizadas

Girón

2023

reuca

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“Ethereum ha tomado lo que era una calculadora de cuatro funciones de un lenguaje de programación en Bitcoin y lo convirtió en una computadora completa” (Fred Ehrsam, s.f.) Ethereum es la segunda criptomoneda de mayor capitalización del mercado, solo detrás de Bitcoin; sin embargo, la naturaleza y potencial es distinta y de una mayor utilidad en el largo plazo. Como se está conociendo en la actualidad, Bitcoin se está perfilando como el dinero de las próximas generaciones; por dicha razón, y ante el auge de nuevas criptomonedas, muchos se preguntan qué es Ethereum. Realidad Empresarial No. 15, 2023: 45-49.

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“…This paper simulates the acquisition of constructions by incrementally increasing the amount of exposure: 100k words, 200k words, 300k words and so on up to 2 million words (Alishahi and Stevenson, 2008;Matusevych et al, 2013;Beekhuizen et al, 2015). This provides a series of grammars, each representing a different state in the learning process.…”

Section: Exposure and Convergencementioning

confidence: 99%

Learned Construction Grammars Converge Across Registers Given Increased Exposure

Dunn

Madabushi

2021

Proceedings of the 25th Conference on Computational Natural Language Learning

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This paper measures the impact of increased exposure on whether learned construction grammars converge onto shared representations when trained on data from different registers. Register influences the frequency of constructions, with some structures common in formal but not informal usage. We expect that a grammar induction algorithm exposed to different registers will acquire different constructions. To what degree does increased exposure lead to the convergence of register-specific grammars? The experiments in this paper simulate language learning in 12 languages (half Germanic and half Romance) with corpora representing three registers (Twitter, Wikipedia, Web). These simulations are repeated with increasing amounts of exposure, from 100k to 2 million words, to measure the impact of exposure on the convergence of grammars. The results show that increased exposure does lead to converging grammars across all languages. In addition, a shared core of register-universal constructions remains constant across increasing amounts of exposure.

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A Usage-Based Model of Early Grammatical Development

Cited by 8 publications

References 20 publications

Bootstrapping language acquisition

Bootstrapping language acquisition

Ethereum y las finanzas descentralizadas

Learned Construction Grammars Converge Across Registers Given Increased Exposure

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