We present a model of morphological segmentation that jointly learns to segment and restore orthographic changes, e.g., funniest → fun-y-est. We term this form of analysis canonical segmentation and contrast it with the traditional surface segmentation, which segments a surface form into a sequence of substrings, e.g., funniest → funn-i-est. We derive an importance sampling algorithm for approximate inference in the model and report experimental results on English, German and Indonesian.
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