Standard
Development of Kazakh Named Entity Recognition Models. / Akhmed-Zaki, Darkhan; Mansurova, Madina; Barakhnin, Vladimir et al.
Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings. ed. / Ngoc Thanh Nguyen; Ngoc Thanh Nguyen; Bao Hung Hoang; Cong Phap Huynh; Dosam Hwang; Bogdan Trawinski; Gottfried Vossen. Springer Science and Business Media Deutschland GmbH, 2020. p. 697-708 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12496 LNAI).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Akhmed-Zaki, D, Mansurova, M
, Barakhnin, V, Kubis, M, Chikibayeva, D & Kyrgyzbayeva, M 2020,
Development of Kazakh Named Entity Recognition Models. in NT Nguyen, NT Nguyen, BH Hoang, CP Huynh, D Hwang, B Trawinski & G Vossen (eds),
Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12496 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 697-708, 12th International Conference on Computational Collective Intelligence, ICCCI 2020, Da Nang, Viet Nam,
30.11.2020.
https://doi.org/10.1007/978-3-030-63007-2_54APA
Akhmed-Zaki, D., Mansurova, M.
, Barakhnin, V., Kubis, M., Chikibayeva, D., & Kyrgyzbayeva, M. (2020).
Development of Kazakh Named Entity Recognition Models. In N. T. Nguyen, N. T. Nguyen, B. H. Hoang, C. P. Huynh, D. Hwang, B. Trawinski, & G. Vossen (Eds.),
Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings (pp. 697-708). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12496 LNAI). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-3-030-63007-2_54Vancouver
Akhmed-Zaki D, Mansurova M
, Barakhnin V, Kubis M, Chikibayeva D, Kyrgyzbayeva M.
Development of Kazakh Named Entity Recognition Models. In Nguyen NT, Nguyen NT, Hoang BH, Huynh CP, Hwang D, Trawinski B, Vossen G, editors, Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 697-708. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-63007-2_54
Author
Akhmed-Zaki, Darkhan ; Mansurova, Madina
; Barakhnin, Vladimir et al. /
Development of Kazakh Named Entity Recognition Models. Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings. editor / Ngoc Thanh Nguyen ; Ngoc Thanh Nguyen ; Bao Hung Hoang ; Cong Phap Huynh ; Dosam Hwang ; Bogdan Trawinski ; Gottfried Vossen. Springer Science and Business Media Deutschland GmbH, 2020. pp. 697-708 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{ff4eb4647fa24c728c798166b514f06e,
title = "Development of Kazakh Named Entity Recognition Models",
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.",
keywords = "BERT, Bi-LSTM, Conditional random fields, Named entity recognition",
author = "Darkhan Akhmed-Zaki and Madina Mansurova and Vladimir Barakhnin and Marek Kubis and Darya Chikibayeva and Marzhan Kyrgyzbayeva",
note = "Funding Information: Acknowledgements. This work was supported in part under grants of Foundation of Ministry of Education and Science of the Republic of Kazakhstan AP05132933 – “Development of a system for knowledge extraction from heterogeneous data sources to improve the quality of decision-making” (2018–2020) and O.0856 BR05236340 – «Creation of high-performance intelligent technologies for analysis and decision making for the “logistics-agglomeration”; system in the framework of the digital economy of the Republic of Kazakhstan» (2018–2020). Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 12th International Conference on Computational Collective Intelligence, ICCCI 2020 ; Conference date: 30-11-2020 Through 03-12-2020",
year = "2020",
doi = "10.1007/978-3-030-63007-2_54",
language = "English",
isbn = "9783030630065",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "697--708",
editor = "Nguyen, {Ngoc Thanh} and Nguyen, {Ngoc Thanh} and Hoang, {Bao Hung} and Huynh, {Cong Phap} and Dosam Hwang and Bogdan Trawinski and Gottfried Vossen",
booktitle = "Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings",
address = "Germany",
}
RIS
TY - GEN
T1 - Development of Kazakh Named Entity Recognition Models
AU - Akhmed-Zaki, Darkhan
AU - Mansurova, Madina
AU - Barakhnin, Vladimir
AU - Kubis, Marek
AU - Chikibayeva, Darya
AU - Kyrgyzbayeva, Marzhan
N1 - Funding Information:
Acknowledgements. This work was supported in part under grants of Foundation of Ministry of Education and Science of the Republic of Kazakhstan AP05132933 – “Development of a system for knowledge extraction from heterogeneous data sources to improve the quality of decision-making” (2018–2020) and O.0856 BR05236340 – «Creation of high-performance intelligent technologies for analysis and decision making for the “logistics-agglomeration”; system in the framework of the digital economy of the Republic of Kazakhstan» (2018–2020).
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - BERT
KW - Bi-LSTM
KW - Conditional random fields
KW - Named entity recognition
UR - http://www.scopus.com/inward/record.url?scp=85097528781&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-63007-2_54
DO - 10.1007/978-3-030-63007-2_54
M3 - Conference contribution
AN - SCOPUS:85097528781
SN - 9783030630065
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 697
EP - 708
BT - Computational Collective Intelligence - 12th International Conference, ICCCI 2020, Proceedings
A2 - Nguyen, Ngoc Thanh
A2 - Nguyen, Ngoc Thanh
A2 - Hoang, Bao Hung
A2 - Huynh, Cong Phap
A2 - Hwang, Dosam
A2 - Trawinski, Bogdan
A2 - Vossen, Gottfried
PB - Springer Science and Business Media Deutschland GmbH
T2 - 12th International Conference on Computational Collective Intelligence, ICCCI 2020
Y2 - 30 November 2020 through 3 December 2020
ER -
