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