BERT applications in natural language processing: a review
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AKDMFHAHV" target="_blank" >RIV/00216208:11320/26:KDMFHAHV - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/s10462-025-11162-5" target="_blank" >http://dx.doi.org/10.1007/s10462-025-11162-5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10462-025-11162-5" target="_blank" >10.1007/s10462-025-11162-5</a>
Alternative languages
Result language
angličtina
Original language name
BERT applications in natural language processing: a review
Original language description
BERT (Bidirectional Encoder Representations from Transformers) has revolutionized Natural Language Processing (NLP) by significantly enhancing the capabilities of language models. This review study examines the complex nature of BERT, including its structure, utilization in different NLP tasks, and the further development of its design via modifications. The study thoroughly analyses the methodological aspects, conducting a comprehensive analysis of the planning process, the implemented procedures, and the criteria used to decide which data to include or exclude in the evaluation framework. In addition, the study thoroughly examines the influence of BERT on several NLP tasks, such as Sentence Boundary Detection, Tokenization, Grammatical Error Detection and Correction, Dependency Parsing, Named Entity Recognition, Part of Speech Tagging, Question Answering Systems, Machine Translation, Sentiment analysis, fake review detection and Cross-lingual transfer learning. The review study adds to the current literature by integrating ideas from multiple sources, explicitly emphasizing the problems and prospects in BERT-based models. The objective is to comprehensively comprehend BERT and its implementations, targeting both experienced researchers and novices in the domain of NLP. Consequently, the present study is expected to inspire more research endeavors, promote innovative adaptations of BERT, and deepen comprehension of its extensive capabilities in various NLP applications. The results presented in this research are anticipated to influence the advancement of future language models and add to the ongoing discourse on enhancing technology for understanding natural language. © The Author(s) 2025.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Artificial Intelligence Review
ISSN
0269-2821
e-ISSN
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Volume of the periodical
58
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
166
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105000171889