Methods and Algorithms for Unsupervised Learning of Morphology
15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing), Kathmandu, Nepal, 6 - 12 April 2014, vol.8403, pp.177-205, (Full Text)
- 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
- Open Archive Collection: AVESIS Open Access Collection
- 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.