Methods and Algorithms for Unsupervised Learning of Morphology
15th Annual Conference on Intelligent Text Processing and Computational Linguistics (CICLing), Kathmandu, Nepal, 6 - 12 Nisan 2014, cilt.8403, ss.177-205, (Tam Metin Bildiri)
- 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
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- 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.