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 wordlevel 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. 1
REF use a discriminative model for unsupervised morphological segmentation that integrates orthographic and semantic properties of words.
An Unsupervised Method for Uncovering Morphological Chains
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science Engineering