Abstract
The broken plural in Arabic is a canonical example of nonconcatenative morphology. We discuss the supervised and unsupervised learning of this type of transduction using different techniques, based on the use of stochastic transducers, trained with the Expectation-Maximisation algorithm. A basic method for supervised learning using the transducers is discussed and then a more advanced technique using a memory-based learning technique with a distance derived from the Fisher kernel of the model. We then discuss how these algorithms can be employed for unsupervised learning, modelling the alignment between the strings as a hidden variable
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Clark, A. (2007). Supervised and Unsupervised Learning of Arabic Morphology. In: Soudi, A., Bosch, A.v., Neumann, G. (eds) Arabic Computational Morphology. Text, Speech and Language Technology, vol 38. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6046-5_10
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DOI: https://doi.org/10.1007/978-1-4020-6046-5_10
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