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Unsupervised Language Acquisition: Theory and Practice
[article]
2002
arXiv
pre-print
In this thesis I present various algorithms for the unsupervised machine learning of aspects of natural languages using a variety of statistical models. The scientific object of the work is to examine the validity of the so-called Argument from the Poverty of the Stimulus advanced in favour of the proposition that humans have language-specific innate knowledge. I start by examining an a priori argument based on Gold's theorem, that purports to prove that natural languages cannot be learned, and
arXiv:cs/0212024v1
fatcat:lsrnihurufcrrk76ygfmlpk4cu