Morphological segmentation using Dirichlet process based bayesian non-parametric models


Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Graduate School of Informatics, Cognitive Science, Turkey

Approval Date: 2016

Student: SERKAN KUMYOL

Supervisor: HÜSEYİN CEM BOZŞAHİN

Abstract:

This study, will try to explore models explaining distributional properties of morphology within the morphological segmentation task. There are di erent learning approaches to the morphological segmentation task based on supervised, semi-supervised and unsupervised learning. The existing systems and how well semi-supervised and unsupervised non-parametric Bayesian models t to the segmentation task will be investigated. Furthermore, the role of occurrence independent and co-occurrence based models in morpheme segmentation will be investigated.