David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
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.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Shalom Lappin with S. Shieber, Machine Learning Theory and Practice as a Source of Insight Into Universal Grammar.
Bryan R. Gibson, Timothy T. Rogers & Xiaojin Zhu (2013). Human Semi-Supervised Learning. Topics in Cognitive Science 5 (1):132-172.
S. Russell (1991). Inductive Learning by Machines. Philosophical Studies 64 (October):37-64.
Martin Možina, Jure Žabkar, Trevor Bench-Capon & Ivan Bratko (2005). Argument Based Machine Learning Applied to Law. Artificial Intelligence and Law 13 (1):53-73.
Gilbert Harman & Sanjeev Kulkarni, Statistical Learning Theory as a Framework for the Philosophy of Induction.
Kuo-Chin Chang, Tzung-Pei Hong & Shian-Shyong Tseng (1996). Machine Learning by Imitating Human Learning. Minds and Machines 6 (2):203-228.
Richard J. Tunney & David R. Shanks (2003). Does Opposition Logic Provide Evidence for Conscious and Unconscious Processes in Artificial Grammar Learning? Consciousness and Cognition 12 (2):201-218.
Sean Fulop & Nick Chater (2013). Editors' Introduction: Why Formal Learning Theory Matters for Cognitive Science. Topics in Cognitive Science 5 (1):3-12.
Jon Williamson (2004). A Dynamic Interaction Between Machine Learning and the Philosophy of Science. Minds and Machines 14 (4):539-549.
Added to index2010-12-22
Total downloads3 ( #294,221 of 1,101,604 )
Recent downloads (6 months)1 ( #292,059 of 1,101,604 )
How can I increase my downloads?