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Most state-of-the-art systems today produce morphological analysis based onlyon orthographic patterns. In contrast, we propose a model for unsupervisedmorphological analysis that integrates orthographic and semantic views ofwords. We model word formation in terms of morphological chains, from basewords to the observed words, breaking the chains into parent-child relations.We use log-linear models with morpheme and word-level features to predictpossible parents, including their modifications, for each word. The limited setof candidate parents for each word render contrastive estimation feasible. Ourmodel consistently matches or outperforms five state-of-the-art systems onArabic, English and Turkish.

An Unsupervised Method for Uncovering Morphological Chains