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LREC
2008

Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation

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Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation
Data Selection has emerged as a common issue in language technologies. We define Data Selection as the choosing of a subset of training data that is most effective for a given task. This paper describes deductive feature detection, one component of a data selection system for machine translation. Feature detection determines whether features such as tense, number, and person are expressed in a language. The database of the The World Atlas of Language Structures provides a gold standard against which to evaluate feature detection. The discovered features can be used as input to a Navigator, which uses active learning to determine which piece of language data is the most important to acquire next.
Jonathan Clark, Robert E. Frederking, Lori S. Levi
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where LREC
Authors Jonathan Clark, Robert E. Frederking, Lori S. Levin
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