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| Autor/in | Yang, Charles |
|---|---|
| Titel | Rage against the Machine: Evaluation Metrics in the 21st Century |
| Quelle | In: Language Acquisition: A Journal of Developmental Linguistics, 24 (2017) 2, S.100-125 (26 Seiten)Infoseite zur Zeitschrift
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1048-9223 |
| DOI | 10.1080/10489223.2016.1274318 |
| Schlagwörter | Language Research; Generative Grammar; Language Acquisition; Cognitive Science; Language Variation; Learning Theories; Linguistic Theory; Language Usage; Bayesian Statistics; Child Language; Linguistic Input |
| Abstract | I review the classic literature in generative grammar and Marr's three-level program for cognitive science to defend the Evaluation Metric as a psychological theory of language learning. Focusing on well-established facts of language variation, change, and use, I argue that optimal statistical principles embodied in Bayesian inference models are ill-suited for language acquisition. Specific attention will be given to the Subset Problem: Indirect negative evidence, which can be attractively formulated in the Bayesian framework, is ineffective when the statistical properties of language are examined in detail. As an alternative, I suggest that the Tolerance Principle (Yang 2016) provides a unified solution for the problem of induction and generalization: It bridges the computational and algorithm levels in Marr's formulation, while retaining the commitment to the formal and empirical constraints in child language development. (As Provided). |
| Anmerkungen | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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