A Probabilistic Model for Learning Concatenative Morphology

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. 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.