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This paper describes a system for unsupervised learning of morphological affixes from texts or word lists. The system is composed of a generative probability model and a search algorithm. Experiments on the Wall Street Journal and the Hansard Corpus (French and English) demonstrate the effectiveness of this approach. The results suggest that more integrated systems for learning both affixes and morphographemic adjustment rules may be feasible. In addition, several definitions and a theorem are developed so that our search algorithm can be formalized in terms of the lattice formed by subsets of suffixes under inclusion. This formalism is expected to be useful for investigating alternative search strategies over the same morphological hypothesis space.
Snover et al. (Mon,) studied this question.
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