Machine learning theory and practice as a source of insight into universal grammar
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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Citations of this work BETA
Complexity in Language Acquisition.Alexander Clark & Shalom Lappin - 2013 - Topics in Cognitive Science 5 (1):89-110.
Explanation and Constructions: Response to Adger.Adele E. Goldberg - 2013 - Mind and Language 28 (4):479-491.
Natural Language and How We Use It: Psychology, Pragmatics, and Presupposition.Ofra Magidor - 2010 - Analysis 70 (1):160-174.
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