A Probabilistic Model for Learning Concatenative Morphology
Abstract
This paper describes a system for the unsupervised learning of morpho- logical suffixes and stems from word lists. The system is composed of a generative probability model and hill-climbing and directed search algo- rithms. By extracting and examining morphologically rich subsets of an input lexicon, the directed search identifies highly productive paradigms. The hill-climbing algorithm then further maximizes the probability of the hypothesis. Quantitative results are shown by measuring the accuracy of the morphological relations identified. Experiments in English and Pol- ish, as well as comparisons with another recent unsupervised morphol- ogy learning algorithm demonstrate the effectiveness of this technique.
Cite
Text
Snover and Brent. "A Probabilistic Model for Learning Concatenative Morphology." Neural Information Processing Systems, 2002.Markdown
[Snover and Brent. "A Probabilistic Model for Learning Concatenative Morphology." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/snover2002neurips-probabilistic/)BibTeX
@inproceedings{snover2002neurips-probabilistic,
title = {{A Probabilistic Model for Learning Concatenative Morphology}},
author = {Snover, Matthew G. and Brent, Michael R.},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {1537-1544},
url = {https://mlanthology.org/neurips/2002/snover2002neurips-probabilistic/}
}