Machine learning theory and practice as a source of insightinto universal grammar
نویسنده
چکیده
In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within linguistic theory as well.
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Universal Grammar and Chaos/Complexity Theory: Where Do They Meet And Where Do They Cross?
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