Version 1
: Received: 25 January 2024 / Approved: 25 January 2024 / Online: 26 January 2024 (03:39:32 CET)
How to cite:
Islam, M.S.; Zhang, L. A Review on BERT: Language Understanding for Different Types of NLP Task. Preprints2024, 2024011857. https://doi.org/10.20944/preprints202401.1857.v1
Islam, M.S.; Zhang, L. A Review on BERT: Language Understanding for Different Types of NLP Task. Preprints 2024, 2024011857. https://doi.org/10.20944/preprints202401.1857.v1
Islam, M.S.; Zhang, L. A Review on BERT: Language Understanding for Different Types of NLP Task. Preprints2024, 2024011857. https://doi.org/10.20944/preprints202401.1857.v1
APA Style
Islam, M.S., & Zhang, L. (2024). A Review on BERT: Language Understanding for Different Types of NLP Task. Preprints. https://doi.org/10.20944/preprints202401.1857.v1
Chicago/Turabian Style
Islam, M.S. and Long Zhang. 2024 "A Review on BERT: Language Understanding for Different Types of NLP Task" Preprints. https://doi.org/10.20944/preprints202401.1857.v1
Abstract
In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful.
Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.
Keywords
Natural Language Processing (NLP); BERT; Language Model; Transfer Learning; Transformers
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The commenter has declared there is no conflict of interests.
Comment:
This is very useful article for understanding various kind of NLP tasks . Its very important in large languages model. BERT model discrive in this article very effectively. So good luck for authors.
Commenter:
The commenter has declared there is no conflict of interests.