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http://hdl.handle.net/1903/6984
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| Title: | Necessary Bias in Natural Language Learning |
| Authors: | Pearl, Lisa Sue |
| Advisors: | Weinberg, Amy |
| Department/Program: | Linguistics |
| Type: | Dissertation |
| Sponsors: | Digital Repository at the University of Maryland
University of Maryland (College Park, Md.) |
| Keywords: | Language, Linguistics (0290)
Computer Science (0984)
Psychology, Developmental (0620)
language learnability, intake filtering, computational modeling, discrete representations, probabilistic learning |
| Issue Date: | 8-May-2007 |
| Abstract: | This 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 ... |
| URI: | http://hdl.handle.net/1903/6984 |
| Appears in Collections: | Linguistics Theses and Dissertations
UM Theses and Dissertations
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| umi-umd-4492.pdf | | 3547Kb | Adobe PDF | 153 | View/Open |
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