A Phonological Approach to the Unsupervised Learning of Root-and-Pattern Morphology
A Phonological Approach to the Unsupervised Learning of Root-and-Pattern Morphology
This contribution describes an algorithm for the unsupervised learning of root-and-pattern morphology. The algorithm relies on a phonological heuristic to bootstrap the morphological analysis and identify a preliminary set of reliable roots and patterns. The analysis is then incrementally extended based on the minimum description length principle, in line with the approach to morphological learning embodied in John Goldsmith’s Linguistica algorithm. The algorithm is implemented as a computer program named Arabica and evaluated with regard to its ability to learn the system of Arabic noun plurals. The tension between the universality of the consonant-vowel distinction and the specificity of root-and-pattern morphology turns out to be crucial for understanding the strengths and weaknesses of this approach.
Keywords: Arabic, unsupervised learning, root, pattern, morphology, minimum description length
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