Type of Document Master's Thesis Author McCloy, Daniel Richard URN etd-03012007-112716 Title Automated Adaptation Between Kiranti Languages Degree Master of Arts Department Linguistics Advisory Committee
Advisor Name Title Dr. Anthony Mattina Committee Chair Dr. David E. Watters Committee Member Dr. Mizuki Miyashita Committee Member Dr. Nancy Mattina Committee Member Keywords
- Kiranti typology
- automated adaptation
Date of Defense 2006-11-30 Availability unrestricted AbstractMcCloy, Daniel, M.A., December 2006 Linguistics
Automated Adaptation Between Kiranti Languages
Chairperson: Dr. Anthony Mattina
Minority language communities that are seeking to develop their language may be hampered by a lack of vernacular materials. Large volumes of such materials may be available in a related language. Automated adaptation holds potential to enable these large volumes of materials to be efficiently translated into the resource-scarce language.
I describe a project to assess the feasibility of automatically adapting text between Limbu and Yamphu, two languages in Nepal’s Kiranti grouping. The approaches taken—essentially a transfer-based system partially hybridized with a Kiranti-specific interlingua—are placed in the context of machine translation efforts world-wide.
A key principle embodied in this strategy is that adaptation can transcend the structural obstacles by taking advantage of functional commonalities. That is, what matters most for successful adaptation is that the languages “care about the same kinds of things.” I examine various typological phenomena of these languages to assess this degree of functional
commonality. I look at the types of features marked on the finite verb, case-marking systems, the encoding of vertical deixis, object-incorporated verbs, and nominalization issues.
As this Kiranti adaptation goal involves adaptation into multiple target languages, I also present a disambiguation strategy that ensures that the manual disambiguation performed for one target language is fed back into the system, such that the same disambiguation will not need to be performed again for other target languages.
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