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.. | |||||||||
| Keywords | No keywords specified (fix it) | |||||||||
| Categories | No categories specified (fix it) | |||||||||
| Options |
|
|||||||||
Download options| PhilPapers Archive |
Upload a copy of this paper Check publisher's policy on self-archival Papers currently archived: 5,701 |
| External links |
  Try with proxy. |
| Through your library | Only published papers are available at libraries |
Similar books and articlesAlexander Clark & Shalom Lappin (2013). Complexity in Language Acquisition. Topics in Cognitive Science 5 (1):89-110.
Anat Ninio (2006). Language and the Learning Curve: A New Theory of Syntactic Development. OUP Oxford.
Afsaneh Fazly, Afra Alishahi & Suzanne Stevenson (2010). A Probabilistic Computational Model of Cross-Situational Word Learning. Cognitive Science 34 (6):1017-1063.
Anne S. Hsu, Nick Chater & Paul Vitányi (2013). Language Learning From Positive Evidence, Reconsidered: A Simplicity-Based Approach. Topics in Cognitive Science 5 (1):35-55.
Paul Vogt & Andrew D. M. Smith (2005). Learning Colour Words is Slow: A Cross-Situational Learning Account. Behavioral and Brain Sciences 28 (4):509-510.
Erik D. Thiessen & Philip I. Pavlik (2013). iMinerva: A Mathematical Model of Distributional Statistical Learning. Cognitive Science 37 (2):310-343.
Fiona Cowie (1997). The Logical Problem of Language Acquisition. Synthese 111 (1):17-51.
Andre Kukla (1992). Endogenous Constraints on Inductive Reasoning. Philosophical Psychology 5 (4):411 – 425.
Analytics
Monthly downloads |
Added to index2010-12-22Total downloads6 ( #145,673 of 549,113 )Recent downloads (6 months)1 ( #63,361 of 549,113 )How can I increase my downloads? |
My notes
Discussion
