David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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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
Ofra Magidor (2010). Natural Language and How We Use It: Psychology, Pragmatics, and Presupposition. Analysis 70 (1):160-174.
Alexander Clark & Shalom Lappin (2013). Complexity in Language Acquisition. Topics in Cognitive Science 5 (1):89-110.
Adele E. Goldberg (2013). Explanation and Constructions: Response to Adger. Mind and Language 28 (4):479-491.
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