Methods and Algorithms for Unsupervised Learning of Morphology


Creative Commons License

Can B., Manandhar S.

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

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 8403
  • Doi Numarası: 10.1038/s41467-019-10836-3
  • Basıldığı Şehir: Kathmandu
  • Basıldığı Ülke: Nepal
  • Sayfa Sayıları: ss.177-205
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.