An unsupervised method for uncovering morphological chains
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Jaakkola_An unsupervised.pdf
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Author(s)
Narasimhan, Karthik Rajagopal
Barzilay, Regina
Jaakkola, Tommi S.
Date Issued
March 2015
Journal
Transactions of the Association for Computational Linguistics
Publisher
Association for Computational Linguistics
Citation
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
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/458
