A Probabilistic Model for Learning Concatenative Morphology
Matthew Snover, Michael Richard Brent · 2002
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. 1