no code implementations • COLING 2022 • Alymzhan Toleu, Gulmira Tolegen, Rustam Mussabayev
This paper presents a language-independent approach for morphological disambiguation which has been regarded as an extension of POS tagging, jointly predicting complex morphological tags.
no code implementations • 17 Jul 2020 • Gulmira Tolegen, Alymzhan Toleu, Orken Mamyrbayev, Rustam Mussabayev
We present several neural networks to address the task of named entity recognition for morphologically complex languages (MCL).
no code implementations • LREC 2020 • Gulmira Tolegen, Alymzhan Toleu, Rustam Mussabayev
This paper presents an approach of voted perceptron for morphological disambiguation for the case of Kazakh language.
no code implementations • ACL 2017 • Alymzhan Toleu, Gulmira Tolegen, Aibek Makazhanov
We develop a language-independent, deep learning-based approach to the task of morphological disambiguation.