Show simple item record

dc.contributor.advisorWeinberg, Amyen_US
dc.contributor.authorPearl, Lisa Sueen_US
dc.date.accessioned2007-06-22T05:39:00Z
dc.date.available2007-06-22T05:39:00Z
dc.date.issued2007-05-08
dc.identifier.urihttp://hdl.handle.net/1903/6984
dc.description.abstractThis dissertation investigates the mechanism of language acquisition given the boundary conditions provided by linguistic representation and the time course of acquisition. Exploration of the mechanism is vital once we consider the complexity of the system to be learned and the non-transparent relationship between the observable data and the underlying system. It is not enough to restrict the potential systems the learner could acquire, which can be done by defining a finite set of parameters the learner must set. Even supposing that the system is defined by n binary parameters, we must still explain how the learner converges on the correct system(s) out of the possible 2^n systems, using data that is often highly ambiguous and exception-filled. The main discovery from the case studies presented here is that learners can in fact succeed provided they are biased to only use a subset of the available input that is perceived as a cleaner representation of the underlying system. The case studies are embedded in a framework that conceptualizes language learning as three separable components, assuming that learning is the process of selecting the best-fit option given the available data. These components are (1) a defined hypothesis space, (2) a definition of the data used for learning (data intake), and (3) an algorithm that updates the learner's belief in the available hypotheses, based on data intake. One benefit of this framework is that components can be investigated individually. Moreover, defining the learning components in this somewhat abstract manner allows us to apply the framework to a range of language learning problems and linguistics domains. In addition, we can combine discrete linguistic representations with probabilistic methods and so account for the gradualness and variation in learning that human children display. The tool of exploration for these case studies is computational modeling, which proves itself very useful in addressing the feasibility, sufficiency, and necessity of data intake filtering since these questions would be very difficult to address with traditional experimental techniques. In addition, the results of computational modeling can generate predictions that can then be tested experimentally.en_US
dc.format.extent3632361 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleNecessary Bias in Natural Language Learningen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentLinguisticsen_US
dc.subject.pqcontrolledLanguage, Linguisticsen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pqcontrolledPsychology, Developmentalen_US
dc.subject.pquncontrolledlanguage learnabilityen_US
dc.subject.pquncontrolledintake filteringen_US
dc.subject.pquncontrolledcomputational modelingen_US
dc.subject.pquncontrolleddiscrete representationsen_US
dc.subject.pquncontrolledprobabilistic learningen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record