Toward Active Learning in Data Selection: Automatic Discovery of Language Features During Elicitation
Jonathan H. Clark, Robert Frederking, Lori S. Levin · 2008
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.