This paper is a survey of methods and algorithms for unsupervised learning of morphology. We provide a description of the methods and algorithms used for morphological segmentation from a computational linguistics point of view. We survey morphological segmentation methods covering methods based on MDL minimum description length, MLE maximum likelihood estimation, MAP maximum a posteriori, parametric and non-parametric Bayesian approaches. A review of the evaluation schemes for unsupervised morphological segmentation is also provided along with a summary of evaluation results on the Morpho Challenge evaluations.
['Burcu Can', 'Suresh Manandhar']
Methods and Algorithms for Unsupervised Learning of Morphology