Development of Kazakh Named Entity Recognition Models

Darkhan Akhmed-Zaki, Madina Mansurova, Vladimir B. Barakhnin, Marek Kubis, Darya Chikibayeva, Marzhan Kyrgyzbayeva

Abstract: Named entity recognition is one of the important tasks in natural language processing. Its practical application can be found in various areas such as speech recognition, information retrieval, filtering, etc. Nowadays there are a variety of available methods for implementing named entity recognition. In this work we experimented with three models and compared the performances of machine learning based models and probabilistic sequence modeling method on the task of Kazakh language named entity recognition. We considered three models based on BERT, Bi-LSTM and CRF baseline. In the future these models can be parts of an ensemble learning system for name entity recognition in order to achieve better performance results.