An unsupervised method for uncovering morphological chains
Author(s)Narasimhan, Karthik Rajagopal; Barzilay, Regina; Jaakkola, Tommi S.
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Transactions of the Association for Computational Linguistics
Association for Computational Linguistics
Narasimhan, Karthik, Regina Barzilay, and Tommi Jaakkola. "An unsupervised method for uncovering morphological chains." Transactions of the Association for Computational Linguistics, Vol. 3 (2015). © 2015 Association for Computational Linguistics
Final published version