Development of Kazakh Named Entity Recognition Models

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review


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.

Original languageEnglish
Title of host publicationComputational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings
EditorsNgoc Thanh Nguyen, Ngoc Thanh Nguyen, Bao Hung Hoang, Cong Phap Huynh, Dosam Hwang, Bogdan Trawinski, Gottfried Vossen
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030630065
Publication statusPublished - 2020
Event12th International Conference on Computational Collective Intelligence, ICCCI 2020 - Da Nang, Viet Nam
Duration: 30 Nov 20203 Dec 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12496 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Computational Collective Intelligence, ICCCI 2020
CountryViet Nam
CityDa Nang


  • BERT
  • Bi-LSTM
  • Conditional random fields
  • Named entity recognition


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