Computational Learning Theory and Language Acquisition

Abstract

Computational learning theory explores the limits of learnability. Studying language acquisition from this perspective involves identifying classes of languages that are learnable from the available data, within the limits of time and computational resources available to the learner. Different models of learning can yield radically different learnability results, where these depend on the assumptions of the model about the nature of the learning process, and the data, time, and resources that learners have access to. To the extent that such assumptions accurately reflect human language learning, a model that invokes them can offer important insights into the formal properties of natural languages, and the way in which their representations might be efficiently acquired. In this chapter we consider several computational learning models that have been applied to the language learning task. Some of these have yielded results that suggest that the class of natural languages cannot be efficiently learned from the primary linguistic data (PLD) available to children, through..

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 74,174

External links

  • This entry has no external links. Add one.
Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

  • Only published works are available at libraries.

Similar books and articles

Complexity in Language Acquisition.Alexander Clark & Shalom Lappin - 2013 - Topics in Cognitive Science 5 (1):89-110.
Endogenous Constraints on Inductive Reasoning.Andre Kukla - 1992 - Philosophical Psychology 5 (4):411 – 425.

Analytics

Added to PP
2010-12-22

Downloads
31 (#373,273)

6 months
1 (#413,813)

Historical graph of downloads
How can I increase my downloads?

Author's Profile

Alexander Clark
University of Wisconsin, Madison

References found in this work

No references found.

Add more references