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Academic Paper


Title: An empirical generative framework for computational modeling of language acquisition
Author: Heidi R Waterfall
Email: click here TO access email
Institution: Cornell University
Author: Ben Sandbank
Institution: Tel Aviv University
Author: Luca Onnis
Institution: University of Hawaii
Author: Shimon Edelman
Email: click here TO access email
Homepage: http://kybele.psych.cornell.edu/~edelman
Institution: Cornell University
Linguistic Field: Computational Linguistics; Language Acquisition
Abstract: This paper reports progress in developing a computer model of language acquisition in the form of (1) a generative grammar that is (2) algorithmically learnable from realistic corpus data, (3) viable in its large-scale quantitative performance and (4) psychologically real. First, we describe new algorithmic methods for unsupervised learning of generative grammars from raw CHILDES data and give an account of the generative performance of the acquired grammars. Next, we summarize findings from recent longitudinal and experimental work that suggests how certain statistically prominent structural properties of child-directed speech may facilitate language acquisition. We then present a series of new analyses of CHILDES data indicating that the desired properties are indeed present in realistic child-directed speech corpora. Finally, we suggest how our computational results, behavioral findings, and corpus-based insights can be integrated into a next-generation model aimed at meeting the four requirements of our modeling framework.

CUP AT LINGUIST

This article appears IN Journal of Child Language Vol. 37, Issue 3, which you can READ on Cambridge's site or on LINGUIST .



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