Language Learning in Large Parameter Spaces
نویسنده
چکیده
Various theories of linguistics have proposed that the differences among natural languages can be parameterized. Certainly syntactic theories such as Principles and Parameters (Chomsky, 1981) assume the existence of such parameters. Along with the problem of defining parameters, we need to address the problem of a child’s acquisition of the settings of these parameters. Several algorithms for parameter setting have been proposed and examined on small spaces. Unless we have a realistic space to study, we cannot fully understand the predictions of these algorithms. Having an implemented computational model of these algorithms is important for studying them at greater depths. This study examines one such parameter-setting algorithm in realistic spaces.
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