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EMNLP
2011

Universal Morphological Analysis using Structured Nearest Neighbor Prediction

8 years 9 months ago
Universal Morphological Analysis using Structured Nearest Neighbor Prediction
In this paper, we consider the problem of unsupervised morphological analysis from a new angle. Past work has endeavored to design unsupervised learning methods which explicitly or implicitly encode inductive biases appropriate to the task at hand. We propose instead to treat morphological analysis as a structured prediction problem, where languages with labeled data serve as training examples for unlabeled languages, without the assumption of parallel data. We define a universal morphological feature space in which every language and its morphological analysis reside. We develop a novel structured nearest neighbor prediction method which seeks to find the morphological analysis for each unlabeled language which lies as close as possible in the feature space to a training language. We apply our model to eight inflecting languages, and induce nominal morphology with substantially higher accuracy than a traditional, MDLbased approach. Our analysis indicates that accuracy continues to...
Young-Bum Kim, João Graça, Benjamin
Added 20 Dec 2011
Updated 20 Dec 2011
Type Journal
Year 2011
Where EMNLP
Authors Young-Bum Kim, João Graça, Benjamin Snyder
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