Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain

The paper presents the results of applying the BERT representation model in the named entity recognition task for the cybersecurity domain in Russian. Several variants of the model were investigated. The best results were obtained using the BERT model, trained on the target collection of information...

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Detalles Bibliográficos
Autores principales: Tikhomirov, Mikhail, Loukachevitch, N., Sirotina, Anastasiia, Dobrov, Boris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298172/
http://dx.doi.org/10.1007/978-3-030-51310-8_2
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author Tikhomirov, Mikhail
Loukachevitch, N.
Sirotina, Anastasiia
Dobrov, Boris
author_facet Tikhomirov, Mikhail
Loukachevitch, N.
Sirotina, Anastasiia
Dobrov, Boris
author_sort Tikhomirov, Mikhail
collection PubMed
description The paper presents the results of applying the BERT representation model in the named entity recognition task for the cybersecurity domain in Russian. Several variants of the model were investigated. The best results were obtained using the BERT model, trained on the target collection of information security texts. We also explored a new form of data augmentation for the task of named entity recognition.
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spelling pubmed-72981722020-06-17 Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain Tikhomirov, Mikhail Loukachevitch, N. Sirotina, Anastasiia Dobrov, Boris Natural Language Processing and Information Systems Article The paper presents the results of applying the BERT representation model in the named entity recognition task for the cybersecurity domain in Russian. Several variants of the model were investigated. The best results were obtained using the BERT model, trained on the target collection of information security texts. We also explored a new form of data augmentation for the task of named entity recognition. 2020-05-26 /pmc/articles/PMC7298172/ http://dx.doi.org/10.1007/978-3-030-51310-8_2 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Tikhomirov, Mikhail
Loukachevitch, N.
Sirotina, Anastasiia
Dobrov, Boris
Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain
title Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain
title_full Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain
title_fullStr Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain
title_full_unstemmed Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain
title_short Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain
title_sort using bert and augmentation in named entity recognition for cybersecurity domain
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298172/
http://dx.doi.org/10.1007/978-3-030-51310-8_2
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