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An empirical generative framework for computational modeling of language acquisition
2010
Journal of Child Language
A B S T R A C T 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
doi:10.1017/s0305000910000024
pmid:20420744
fatcat:272iidhhyzaqdhaofqxamyj54u