Methods and Algorithms for Unsupervised Learning of Morphology

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Can B., Manandhar S.

15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing), Kathmandu, Nepal, 6 - 12 April 2014, vol.8403, pp.177-205 identifier identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 8403
  • Doi Number: 10.1038/s41467-019-10836-3
  • City: Kathmandu
  • Country: Nepal
  • Page Numbers: pp.177-205
  • Hacettepe University Affiliated: Yes

Abstract

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.