Paper: Universal Morphological Analysis using Structured Nearest Neighbor Prediction

ACL ID D11-1030
Title Universal Morphological Analysis using Structured Nearest Neighbor Prediction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

In this paper, we consider the problem of un- supervised morphological analysis from a new angle. Past work has endeavored to design un- supervised learning methods which explicitly or implicitly encode inductive biases appropri- ate to the task at hand. We propose instead to treat morphological analysis as a structured prediction problem, where languages with la- beled data serve as training examples for un- labeled languages, without the assumption of parallel data. We define a universal morpho- logical feature space in which every language and its morphological analysis reside. We de- velop a novel structured nearest neighbor pre- diction method which seeks to find the mor- phological analysis for each unlabeled lan- guage which lies as close as possible in the feature space to a traini...