(Nazarbayev University School of Science and Technology, 2016) Azamat, Daiana
This paper presents the results of developing a statistical model for morphological
disambiguation of Kazakh text. Starting with basic assumptions we tried
to cope with the complex morphology of Kazakh language by breaking up lexical
forms across their derivational boundaries into inflectional groups and modeling
their behavior with statistical methods. We also provide maximum likelihood estimates
for the parameters and an effective way to perform disambiguation with
the Viterbi algorithm.