An empirical generative framework for computational modeling of language acquisition

Heidi R. Waterfall, Ben Sandbank, Luca Onnis, Shimon Edelman
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
more » ... indings 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 [*] During the preparation of this paper, Shimon
doi:10.1017/s0305000910000024 pmid:20420744 fatcat:272iidhhyzaqdhaofqxamyj54u