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Journal articles on the topic 'Bidirectional encoder representations from transformers'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Chaudhry, Parinnay. "Bidirectional Encoder Representations from Transformers for Modelling Stock Prices." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (2022): 896–901. http://dx.doi.org/10.22214/ijraset.2022.40406.

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Abstract: Bidirectional Encoder Representations from Transformers (BERT) is a transformer neural network architecture designed for natural language processing (NLP). The model’s architecture allows for an efficient, contextual understanding of words in sentences. Empirical evidence regarding the usage of BERT has proved a high degree of accuracy in NLP tasks such as sentiment analysis and next sentence classification. This study utilises BERT’s sentiment analysis capability, proposes and tests a framework to model a quantitative relation between the news and reportings of a company, and the mo

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Adil Rehman, Khushal Das, Kamlish, and Fazeel Abid. "Sentiment Analysis using Bidirectional Encoder Representations from Transformers." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 61, no. 2 (2024): 153–65. http://dx.doi.org/10.53560/ppasa(61-2)870.

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In the contemporary digital landscape, a significant volume of data is generated through social networks such as Twitter, Facebook, and Instagram. This study presents a method for extracting sentiments from Twitter, focusing on two sentiment-based datasets: the Twitter and emotional sentiments datasets. After extraction and preprocessing, we employed three deep learning models: Recurrent Neural Networks (RNNs), Bidirectional Long Short-Term Memory (BiLSTM), and a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model. We introduced Se-BERT, a model designed for emotio

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Varghese, Nisha, and Shafi Shereef. "DOMAIN-SPECIFIC TOKEN RECOGNITION USING BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS AND SCIBERT." ICTACT Journal on Microelectronics 10, no. 2 (2024): 1817–21. https://doi.org/10.21917/ijme.2024.0314.

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Make machines to read and comprehend information from natural language documents are not an easy task. Machine reading comprehension is a solution to alleviate this issue by extracting the relevant information from the corpus by posing a question based on the context. The problem associated with this knowledge retrieval is in the correct answer extraction from the context with language understanding. The traditional rule-based, keyword search and deep learning approaches are inadequate to infer the right answer from the input context. The Transformer based methodologies are used to excerpt the

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Santos, Brucce Neves Dos, Ricardo Marcondes Marcacini, and Solange Oliveira Rezende. "Multi-Domain Aspect Extraction Using Bidirectional Encoder Representations From Transformers." IEEE Access 9 (2021): 91604–13. http://dx.doi.org/10.1109/access.2021.3089099.

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Pardamean, Amsal, and Hilman F. Pardede. "Tuned bidirectional encoder representations from transformers for fake news detection." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1667. http://dx.doi.org/10.11591/ijeecs.v22.i3.pp1667-1671.

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Online medias are currently the dominant source of Information due to not being limited by time and place, fast and wide distributions. However, inaccurate news, or often referred as fake news is a major problem in news dissemination for online medias. Inaccurate news is information that is not true, that is engineered to cover the real information and has no factual basis. Usually, inaccurate news is made in the form of news that has mass appeal and is presented in the guise of genuine and legitimate news nuances to deceive or change the reader's mind or opinion. Identification of inaccurate

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Pardamean, Amsal, and Hilman F. Pardede. "Tuned bidirectional encoder representations from transformers for fake news detection." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 3 (2021): 1667–71. https://doi.org/10.11591/ijeecs.v22.i3.pp1667-1671.

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Online medias are currently the dominant source of Information due to not being limited by time and place, fast and wide distributions. However, inaccurate news, or often referred as fake news is a major problem in news dissemination for online medias. Inaccurate news is information that is not true, that is engineered to cover the real information and has no factual basis. Usually, inaccurate news is made in the form of news that has mass appeal and is presented in the guise of genuine and legitimate news nuances to deceive or change the reader's mind or opinion. Identification of inaccur

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Zubeiri, Iman, Adnan Souri, and Badr Eddine El Mohajir. "Arabic text diacritization using transformers: a comparative study." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 702. http://dx.doi.org/10.11591/ijai.v14.i1.pp702-711.

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The Arabic language presents challenges for natural language processing (NLP) tasks. One such challenge is diacritization, which involves adding diacritical marks to Arabic text to enhance readability and disambiguation. Diacritics play a crucial role in determining the correct pronunciation, meaning, and grammatical structure of words and sentences. However, Arabic texts are often written without diacritics, making NLP tasks more complex. This study investigates the efficacy of advanced machine learning models in automatic Arabic text diacritization, with a concentrated focus on the Arabic bi

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Iman, Zubeiri, Souri Adnan, and Eddine El Mohajir Badr. "Arabic text diacritization using transformers: a comparative study." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 702–11. https://doi.org/10.11591/ijai.v14.i1.pp702-711.

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The Arabic language presents challenges for natural language processing (NLP) tasks. One such challenge is diacritization, which involves adding diacritical marks to Arabic text to enhance readability and disambiguation. Diacritics play a crucial role in determining the correct pronunciation, meaning, and grammatical structure of words and sentences. However, Arabic texts are often written without diacritics, making NLP tasks more complex. This study investigates the efficacy of advanced machine learning models in automatic Arabic text diacritization, with a concentrated focus on the Arabic bi

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Choi, Yong-Seok, Yo-Han Park, and Kong Joo Lee. "Building a Korean morphological analyzer using two Korean BERT models." PeerJ Computer Science 8 (May 2, 2022): e968. http://dx.doi.org/10.7717/peerj-cs.968.

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A morphological analyzer plays an essential role in identifying functional suffixes of Korean words. The analyzer input and output differ from each other in their length and strings, which can be dealt with by an encoder-decoder architecture. We adopt a Transformer architecture, which is an encoder-decoder architecture with self-attention rather than a recurrent connection, to implement a Korean morphological analyzer. Bidirectional Encoder Representations from Transformers (BERT) is one of the most popular pretrained representation models; it can present an encoded sequence of input words, co

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Arshed, Muhammad Asad, Ștefan Cristian Gherghina, Dur-E.-Zahra Dur-E-Zahra, and Mahnoor Manzoor. "Prediction of Machine-Generated Financial Tweets Using Advanced Bidirectional Encoder Representations from Transformers." Electronics 13, no. 11 (2024): 2222. http://dx.doi.org/10.3390/electronics13112222.

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With the rise of Large Language Models (LLMs), distinguishing between genuine and AI-generated content, particularly in finance, has become challenging. Previous studies have focused on binary identification of ChatGPT-generated content, overlooking other AI tools used for text regeneration. This study addresses this gap by examining various AI-regenerated content types in the finance domain. Objective: The study aims to differentiate between human-generated financial content and AI-regenerated content, specifically focusing on ChatGPT, QuillBot, and SpinBot. It constructs a dataset comprising

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Holle, Khadijah Fahmi Hayati, Daurin Nabilatul Munna, and Enggarani Wahyu Ekaputri. "Performance Evaluation of Transformer Models: Scratch, Bart, and Bert for News Document Summarization." Jurnal Teknik Informatika (Jutif) 6, no. 2 (2025): 787–802. https://doi.org/10.52436/1.jutif.2025.6.2.2534.

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This study evaluates the performance of three Transformer models: Transformer from Scratch, BART (Bidirectional and Auto-Regressive Transformers), and BERT (Bidirectional Encoder Representations from Transformers) in the task of summarizing news documents. The evaluation results show that BERT excels in understanding the bidirectional context of text, with a ROUGE-1 value of 0.2471, ROUGE-2 of 0.1597, and ROUGE-L of 0.1597. BART shows strong ability in de-noising and producing coherent summaries, with a ROUGE-1 value of 0.5239, ROUGE-2 of 0.3517, and ROUGE-L of 0.3683. Transformer from Scratch

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Shirahatti, Abhinandan, Vijay Rajpurohit, and Sanjeev Sannakki. "Fine grained irony classification through transfer learning approach." Computer Science and Information Technologies 4, no. 1 (2023): 43–49. http://dx.doi.org/10.11591/csit.v4i1.p43-49.

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Nowadays irony appears to be pervasive in all social media discussion forums and chats, offering further obstacles to sentiment analysis efforts. The aim of the present research work is to detect irony and its types in English tweets We employed a new system for irony detection in English tweets, and we propose a distilled bidirectional encoder representations from transformers (DistilBERT)light transformer model based on the bidirectional encoder representations from transformers (BERT) architecture, this is further strengthened by the use and design of bidirectional long-short term memory (B

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Shirahatti, Abhinandan, Vijay Rajpurohit, and Sanjeev Sannakki. "Fine grained irony classification through transfer learning approach." Computer Science and Information Technologies 4, no. 1 (2023): 43–49. http://dx.doi.org/10.11591/csit.v4i1.pp43-49.

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Nowadays irony appears to be pervasive in all social media discussion forums and chats, offering further obstacles to sentiment analysis efforts. The aim of the present research work is to detect irony and its types in English tweets We employed a new system for irony detection in English tweets, and we propose a distilled bidirectional encoder representations from transformers (DistilBERT) light transformer model based on the bidirectional encoder representations from transformers (BERT) architecture, this is further strengthened by the use and design of bidirectional long-short term memory (

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Abhinandan, Shirahattii, Rajpurohit Vijay, and Sannakki Sanjeev. "Fine grained irony classification through transfer learning approach." Computer Science and Information Technologies 4, no. 1 (2023): 43–49. https://doi.org/10.11591/csit.v4i1.pp43-49.

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Nowadays irony appears to be pervasive in all social media discussion forums and chats, offering further obstacles to sentiment analysis efforts. The aim of the present research work is to detect irony and its types in English tweets We employed a new system for irony detection in English tweets, and we propose a distilled bidirectional encoder representations from transformers (DistilBERT) light transformer model based on the bidirectional encoder representations from transformers (BERT) architecture, this is further strengthened by the use and design of bidirectional long-short term memory (

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Sumarudin, Muhammad, and Mohammad Syafrullah. "Named Entity Recognition In Electronic Medical Records Based On Hybrid Neural Network And Transformer." Eduvest - Journal of Universal Studies 4, no. 6 (2024): 5263–79. http://dx.doi.org/10.59188/eduvest.v4i6.1473.

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The development of artificial intelligence in the field of health encourages the use of electronic medical records in all health facilities to record health services provided to patients. For hospitals, extracting information from electronic medical records can make it easier for management to make clinical decisions and for researchers to obtain data for research in the medical and nursing fields. The research builds a model of named entity recognition in electronic medical records based on hybrid neural networks, bidirectional encoder representations from transformers, and setting hyperparam

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Liu, Junming, Yuntao Zhao, Yongxin Feng, Yutao Hu, and Xiangyu Ma. "SeMalBERT: Semantic-based malware detection with bidirectional encoder representations from transformers." Journal of Information Security and Applications 80 (February 2024): 103690. http://dx.doi.org/10.1016/j.jisa.2023.103690.

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Sukmawati, Enjeli Cistia, Lintang Suryaningrum, Diva Angelica, and Nur Ghaniaviyanto Ramadhan. "Klasifikasi Berita Palsu Menggunakan Model Bidirectional Encoder Representations From Transformers (BERT)." SisInfo 6, no. 2 (2024): 76–85. https://doi.org/10.37278/sisinfo.v6i2.934.

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Penyebaran informasi palsu menjadi tantangan serius dalam era digital yang terus berkembang, terutama melalui internet dan platform sosial media. Akses mudah terhadap informasi tidak terverifikasi menciptakan tantangan membedakan antara fakta dan hoaks. Salah satu aspek utama yang perlu diatasi adalah mengklasifikasikan berita palsu dengan tingkat akurasi yang tinggi. Berita sebagai sumber informasi aktual memerlukan pengelompokan untuk memfasilitasi akses, namun tidak semua berita dari berbagai sumber memiliki kredibilitas tinggi, terutama dengan adanya fake news. Fake news dapat merugikan in

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Ahmed, Shihab, Moythry Manir Samia, Maksuda Haider Sayma, Md Mohsin Kabir, and M. F. Mridha. "tRF-BERT: A transformative approach to aspect-based sentiment analysis in the bengali language." PLOS ONE 19, no. 9 (2024): e0308050. http://dx.doi.org/10.1371/journal.pone.0308050.

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In recent years, the surge in reviews and comments on newspapers and social media has made sentiment analysis a focal point of interest for researchers. Sentiment analysis is also gaining popularity in the Bengali language. However, Aspect-Based Sentiment Analysis is considered a difficult task in the Bengali language due to the shortage of perfectly labeled datasets and the complex variations in the Bengali language. This study used two open-source benchmark datasets of the Bengali language, Cricket, and Restaurant, for our Aspect-Based Sentiment Analysis task. The original work was based on

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Alkabool, Alia, Sukaina Abdullah, Sadiq Zadeh, and Hani Mahfooz. "Identifying Discourse Elements in Writing by Longformer for NER Token Classification." Iraqi Journal for Electrical and Electronic Engineering 19, no. 1 (2023): 87–92. http://dx.doi.org/10.37917/ijeee.19.1.11.

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Current automatic writing feedback systems cannot distinguish between different discourse elements in students' writing. This is a problem because, without this ability, the guidance provided by these systems is too general for what students want to achieve on arrival. This is cause for concern because automated writing feedback systems are a great tool for combating student writing declines. According to the National Assessment of Educational Progress, less than 30 percent of high school graduates are gifted writers. If we can improve the automatic writing feedback system, we can improve the

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Sowndarya, C. A., Shashi Dahiya, Alka Arora, et al. "Keyword based Hybrid Approach for Aspect based Sentiment Analysis of Course Feedback Data in Education." INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES 21, no. 01 (2025): 125. https://doi.org/10.59467/ijass.2025.21.125.

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This study investigates aspect-based sentiment analysis of educational course feedback using a hybrid approach that combines keyword-based aspect extraction with traditional Machine Learning, Deep Learning, and transformer-based model predictions. The proposed methodology leverages the interpretability of keyword matching alongside the adaptability of Machine Learning, Deep Learning and transformer models to enhance overall performance, particularly when dealing with imbalanced, multi-aspect datasets like those derived from Massive Open Online Courses. The development of the hybrid approach in

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Mohan Elankath, Syam, and Sunitha Ramamirtham. "Sentiment analysis of Malayalam tweets using bidirectional encoder representations from transformers: a study." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (2023): 1817. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1817-1826.

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Sentiment analysis on views and opinions expressed in Indian regional languages has become the current focus of research. But, compared to a globally accepted language like English, research on sentiment analysis in Indian regional languages like Malayalam are very low. One of the major hindrances is the lack of publicly available Malayalam datasets. This work focuses on building a Malayalam dataset for facilitating sentiment analysis on Malayalam texts and studying the efficiency of a pre-trained deep learning model in analyzing the sentiments latent in Malayalam texts. In this work, a Malaya

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Syam, Mohan Elankath, and Ramamirtham Sunitha. "Sentiment analysis of Malayalam tweets using bidirectional encoder representations from transformers: a study." Sentiment analysis of Malayalam tweets using bidirectional encoder representations from transformers: a study 29, no. 3 (2023): 1817–26. https://doi.org/10.11591/ijeecs.v29.i3.pp1817-1826.

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Sentiment analysis on views and opinions expressed in Indian regional languages has become the current focus of research. But, compared to a globally accepted language like English, research on sentiment analysis in Indian regional languages like Malayalam are very low. One of the major hindrances is the lack of publicly available Malayalam datasets. This work focuses on building a Malayalam dataset for facilitating sentiment analysis on Malayalam texts and studying the efficiency of a pre-trained deep learning model in analyzing the sentiments latent in Malayalam texts. In this work, a Malaya

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Yin, Xiaoya, Wu Zhang, Wenhao Zhu, Shuang Liu, and Tengjun Yao. "Improving Sentence Representations via Component Focusing." Applied Sciences 10, no. 3 (2020): 958. http://dx.doi.org/10.3390/app10030958.

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The efficiency of natural language processing (NLP) tasks, such as text classification and information retrieval, can be significantly improved with proper sentence representations. Neural networks such as convolutional neural network (CNN) and recurrent neural network (RNN) are gradually applied to learn the representations of sentences and are suitable for processing sequences. Recently, bidirectional encoder representations from transformers (BERT) has attracted much attention because it achieves state-of-the-art performance on various NLP tasks. However, these standard models do not adequa

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Ihsan Septian, Firdaus, Ivana Lucia Kharisma, Hermanto Hermanto, and Kamdan Kamdan. "Implementasi Metode Bidirectional Encoder Representations from Transformers (BERT) untuk Analisis Sentimen Komentar Pengguna Aplikasi Dana di Instagram." Prosiding TAU SNARS-TEK Seminar Nasional Rekayasa dan Teknologi 3, no. 1 (2024): 201–10. https://doi.org/10.47970/snarstek.v2i1.571.

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Kemajuan teknologi yang pesat saat ini mempengaruhi berbagai aspek kehidupan serta memberi kemudahan serta efisiensi pada berbagai aspek. Penerapan teknologi salah satunya di bidang finansial, yaitu dengan semakin banyak layanan keuangan digital yang memberi kemudahan bagi transaksi keuangan. Salah satu jenis keuangan digital yang banyak digunakan di masyarakat adalah aplikasi Dana. Dana menyediakan layanan yang dapat digunakan penggunanya serta sering memberikan informasi produk melalui akun media sosial Instagram. Feedback serta komentar tentang aplikasi didapatkan dari pengguna. Dengan mene

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Fitrianto, Rizal Akbar, and Arda Surya Editya. "Klasifikasi Tweet Sarkasme Pada Platform X Menggunakan Bidirectional Encoder Representations from Transformers." Jurnal Teknologi Dan Sistem Informasi Bisnis 6, no. 3 (2024): 366–71. http://dx.doi.org/10.47233/jteksis.v6i3.1344.

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X adalah platform digital yang memfasilitasi berbagi pemikiran dan kritik melalui konten tertulis. Sejumlah individu dan organisasi bergantung pada pandangan atau sentimen masyarakat umum saat membuat keputusan. Konsumen umumnya mengandalkan pandangan konsumen lain saat mengevaluasi produk atau layanan yang mereka temui di situs media sosial. Melalui pengawasan aktivitas media sosial, perusahaan yang menjual produk dan layanan dapat memperoleh wawasan tentang emosi yang diekspresikan oleh konsumen terhadap penawaran mereka. Namun, karena keterbatasan menulis, yang tidak mampu mentransmisikan i

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Mohan, Anuraj, Abhilash M. Nair, Bhadra Jayakumar, and Sanjay Muraleedharan. "Sarcasm Detection Using Bidirectional Encoder Representations from Transformers and Graph Convolutional Networks." Procedia Computer Science 218 (2023): 93–102. http://dx.doi.org/10.1016/j.procs.2022.12.405.

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Sjoraida, Diah Fatma, Bucky Wibawa Karya Guna, and Dudi Yudhakusuma. "Analisis Sentimen Film Dirty Vote Menggunakan BERT (Bidirectional Encoder Representations from Transformers)." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 8, no. 2 (2024): 393–404. http://dx.doi.org/10.35870/jtik.v8i2.1580.

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Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan film "Dirty Vote" dari berbagai sumber, seperti media sosial, situs web ulasan film, dan forum online, dengan menggunakan model BERT yang telah di-fine-tuning. Pendekatan ini melibatkan pengumpulan data ulasan, pre-processing data, fine-tuning model BERT, dan evaluasi kinerja model. Hasil penelitian menunjukkan bahwa model BERT mencapai tingkat kinerja yang tinggi dengan akurasi, presisi, recall, dan F1-score yang melebihi ambang batas 0.8 pada dataset validasi. Analisis sentimen dari berbagai sumber mengungkapkan varia

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Argade, Dakshata, Vaishali Khairnar, Deepali Vora, Shruti Patil, Ketan Kotecha, and Sultan Alfarhood. "Multimodal Abstractive Summarization using bidirectional encoder representations from transformers with attention mechanism." Heliyon 10, no. 4 (2024): e26162. http://dx.doi.org/10.1016/j.heliyon.2024.e26162.

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Tejo Arum, Dinenda, Nurchim Nurchim, and Afu Ichsan Pradana. "IMPLEMENTASI BIDIRECTIONAL ENCODER REPRESENTATIONS FROM TRANSFORMERS (BERT) UNTUK KLASIFIKASI SPAM PADA EMAIL." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 2 (2025): 2491–96. https://doi.org/10.36040/jati.v9i2.13114.

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Spam email adalah tantangan utama dalam komunikasi digital, yang memerlukan solusi efektif untuk mendeteksi dan mengeliminasi pesan yang tidak diinginkan. Penelitian ini bertujuan untuk mengimplementasikan model Bidirectional Encoder Representations from Transformers (BERT) dalam klasifikasi email spam. BERT, sebagai model transformasi berbasis deep learning yang telah dioptimalkan untuk pemahaman konteks, mampu memproses dan menganalisis pola linguistik yang kompleks pada data teks. Proses penelitian meliputi pengumpulan dataset email, preprocessing data untuk menghapus noise, pelabelan data,

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Joukhadar, Alaa, Nada Ghneim, and Ghaida Rebdawi. "Impact of Using Bidirectional Encoder Representations from Transformers (BERT) Models for Arabic Dialogue Acts Identification." Ingénierie des systèmes d information 26, no. 5 (2021): 469–75. http://dx.doi.org/10.18280/isi.260506.

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In Human-Computer dialogue systems, the correct identification of the intent underlying a speaker's utterance is crucial to the success of a dialogue. Several researches have studied the Dialogue Act Classification (DAC) task to identify Dialogue Acts (DA) for different languages. Recently, the emergence of Bidirectional Encoder Representations from Transformers (BERT) models, enabled establishing state-of-the-art results for a variety of natural language processing tasks in different languages. Very few researches have been done in the Arabic Dialogue acts identification task. The BERT repres

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Jinu Paulson Siluvai Rathinam, Et al. "Bidirectional Encoder Representations Transformers for Improving CNN-LSTM Covid-19 Disease Detection Classifier." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1513–23. http://dx.doi.org/10.17762/ijritcc.v11i10.8702.

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Early identification of COVID-19 diseased persons is crucial to avoid and prevent the transmission of the SARS-CoV-2 virus. To achieve this, lung Computed Tomography (CT) scan segmentation and categorization models have been broadly developed for COVID-19 diagnosis. Amongst, Multi-Scale function learning with an Attention-based UNet and Marginal Space Deep Ambiguity-attentive Transfer Learning (MS-AUNet-MSDATL) framework is developed to concurrently segment the COVID-19 infected regions and classify their risk levels from the CT/Chest X-Ray (CXR) scans. This model utilizes Convolutional Neural

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Jayaraman, Ashok Kumar, Abirami Murugappan, Tina Esther Trueman, Gayathri Ananthakrishnan, and Ashish Ghosh. "Imbalanced aspect categorization using bidirectional encoder representation from transformers." Procedia Computer Science 218 (2023): 757–65. http://dx.doi.org/10.1016/j.procs.2023.01.056.

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Nabiilah, Ghinaa Zain, Islam Nur Alam, Eko Setyo Purwanto, and Muhammad Fadlan Hidayat. "Indonesian multilabel classification using IndoBERT embedding and MBERT classification." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 1071. http://dx.doi.org/10.11591/ijece.v14i1.pp1071-1078.

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The rapid increase in social media activity has triggered various discussion spaces and information exchanges on social media. Social media users can easily tell stories or comment on many things without limits. However, this often triggers open debates that lead to fights on social media. This is because many social media users use toxic comments that contain elements of racism, radicalism, pornography, or slander to argue and corner individuals or groups. These comments can easily spread and trigger users vulnerable to mental disorders due to unhealthy and unfair debates on social media. Thu

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Kachkou, D. I. "Language modeling and bidirectional coders representations: an overview of key technologies." Informatics 17, no. 4 (2021): 61–72. http://dx.doi.org/10.37661/1816-0301-2020-17-4-61-72.

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The article is an essay on the development of technologies for natural language processing, which formed the basis of BERT (Bidirectional Encoder Representations from Transformers), a language model from Google, showing high results on the whole class of problems associated with the understanding of natural language. Two key ideas implemented in BERT are knowledge transfer and attention mechanism. The model is designed to solve two problems on a large unlabeled data set and can reuse the identified language patterns for effective learning for a specific text processing problem. Architecture Tr

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Kachkou, D. I. "Language modeling and bidirectional coders representations: an overview of key technologies." Informatics 17, no. 4 (2021): 61–72. http://dx.doi.org/10.37661/1816-0301-2020-17-4-61-72.

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The article is an essay on the development of technologies for natural language processing, which formed the basis of BERT (Bidirectional Encoder Representations from Transformers), a language model from Google, showing high results on the whole class of problems associated with the understanding of natural language. Two key ideas implemented in BERT are knowledge transfer and attention mechanism. The model is designed to solve two problems on a large unlabeled data set and can reuse the identified language patterns for effective learning for a specific text processing problem. Architecture Tr

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Farida, Zaemita Wahidatul, and Naim Rochmawati. "Analisis Sentimen Masyarakat terhadap Fenomena Childfree Menggunakan Metode Long Short Term Memory dan Bidirectional Encoder Representations from Transformers di Twitter." Journal of Informatics and Computer Science (JINACS) 5, no. 03 (2024): 369–76. http://dx.doi.org/10.26740/jinacs.v5n03.p369-376.

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Abstrak— Media sosial menjadi sarana bagi masyarakat untuk saling berinteraksi secara virtual. Penggunaan media sosial tidak terbatas pada berinteraksi saja, melainkan menjadi sarana pemanfaatan dalam melakukan penelitian, seperti halnya pada media sosial twitter. Twitter menjadi tempat yang ramai ketika terdapat isu terkini di negara ini bahkan dunia, beberapa isu terkini menjadi perhatian khusus oleh masyarakat, salah satunya tentang isu fenomena childfree, sehingga penelitian ini dilakukan dengan tujuan untuk mengetahui analisis sentimen terhadap fenomena menggunakan metode Long Short Term

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Vincentio, Alfonso Darren, and Seng Hansun. "A Fine-Tuned BART Pre-trained Language Model for the Indonesian Question-Answering Task." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 21398–403. https://doi.org/10.48084/etasr.9828.

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The information extraction process from a given context can be time consuming and a Pre-trained Language Model (PLM) based on the transformer architecture could reduce the time needed to obtain the information. Moreover, PLM is easily fine-tuned to accomplish certain tasks, one of which is the Question-Answering (QA) task. In literature, QA tasks are generally fine-tuned using encoder-based PLMs, such as the Bidirectional Encoder Representations from Transformers (BERT), where the generated answers come from the extraction process of the context. In order to be able to return more abstract ans

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Malik, Andhika, Andhika Putra Gefadri, Elman Sidik, and Alika Putie Syadrina. "SoulScripture: Chatbot using Bidirectional Encoder Representations from Transformers as a Medium of Spiritual Guidance." Khazanah Journal of Religion and Technology 2, no. 1 (2024): 23–27. https://doi.org/10.15575/kjrt.v2i1.822.

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Mental health is an important aspect of human life. Many people face stress, anxiety, and distress daily without adequate support to manage these conditions. Islamic teachings from the Quran and Hadith provide wisdom as a source of inspiration and inner peace. However, accessing and understanding these teachings requires specialized knowledge and often the help of experts. With the advancement of machine learning, these teachings can be made more accessible and accurate. The SoulScripture app offers an innovative solution to support mental health by combining the wisdom of the Quran and Hadith

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Mutawa, A. M., and Sai Sruthi. "A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification." Applied Sciences 15, no. 9 (2025): 4941. https://doi.org/10.3390/app15094941.

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Arabic poetry follows intricate rhythmic patterns known as ‘arūḍ’ (prosody), which makes its automated categorization particularly challenging. While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models—an area not extensively explored for this task. We investigate several pretrained transformer models, including Arabic Bidirectional Encoder Representations from Transformers (Arabic-BERT), BERT base Arabic (AraBERT), Arabic Efficiently Learning an Encoder that Classifies Token Replacem

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Turki, Hussain Mohammed, Essam Al Daoud, Ghassan Samara, et al. "Arabic fake news detection using hybrid contextual features." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 836. http://dx.doi.org/10.11591/ijece.v15i1.pp836-845.

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Technology has advanced and social media users have grown dramatically in the last decade. Because social media makes information easily accessible, some people or organizations distribute false news for political or commercial gain. This news may influence elections and attitudes. Even though English fake news is widely detected and limited, Arabic fake news is hard to recognize owing to a lack of study and data collection. Wara Arabic bidirectional encoder representations from transformers (WaraBERT), a hybrid feature extraction approach, combines word level tokenization with two Arabic bidi

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Abdelfattah, Mohamed Fawzy, Mohamed Waleed Fakhr, and Mohamed Abo Rizka. "ArSentBERT: fine-tuned bidirectional encoder representations from transformers model for Arabic sentiment classification." Bulletin of Electrical Engineering and Informatics 12, no. 2 (2023): 1196–202. http://dx.doi.org/10.11591/eei.v12i2.3914.

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Sentiment analysis in the Arabic language is challenging because of its linguistic complexity. Arabic is complex in words, paragraphs, and sentence structure. Moreover, most Arabic documents contain multiple dialects, writing alphabets, and styles (e.g., Franco-Arab). Nevertheless, fine-tuned bidirectional encoder representations from transformers (BERT) models can provide a reasonable prediction accuracy for Arabic sentiment classification tasks. This paper presents a fine-tuning approach for BERT models for classifying Arabic sentiments. It uses Arabic BERT pre-trained models and tokenizers

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Mohamed, Fawzy Abdelfattah, Waleed Fakhr Mohamed, and Abo Rizka Mohamed. "ArSentBERT: fine-tuned bidirectional encoder representations from transformers model for Arabic sentiment classification." Bulletin of Electrical Engineering and Informatics 12, no. 2 (2023): 1196~1202. https://doi.org/10.11591/eei.v12i1.3914.

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Sentiment analysis in the Arabic language is challenging because of its linguistic complexity. Arabic is complex in words, paragraphs, and sentence structure. Moreover, most Arabic documents contain multiple dialects, writing alphabets, and styles (e.g., Franco-Arab). Nevertheless, fine-tuned bidirectional encoder representations from transformers (BERT) models can provide a reasonable prediction accuracy for Arabic sentiment classification tasks. This paper presents a fine-tuning approach for BERT models for classifying Arabic sentiments. It uses Arabic BERT pre-trained models and tokenizers

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Riaz, Ayesha, Omar Abdulkader, Muhammad Jawad Ikram, and Sadaqat Jan. "Exploring topic modelling: a comparative analysis of traditional and transformer-based approaches with emphasis on coherence and diversity." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1933–48. https://doi.org/10.11591/ijece.v15i2.pp1933-1948.

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Topic modeling (TM) is an unsupervised technique used to recognize hidden or abstract topics in large corpora, extracting meaningful patterns of words (semantics). This paper explores TM within data mining (DM), focusing on challenges and advancements in extracting insights from datasets, especially from social media platforms (SMPs). Traditional techniques like latent Dirichlet allocation (LDA), alongside newer methodologies such as bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPT), and extra long-term memory networks (XLNet) are examine

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Putri, Cindy Alifia. "Analisis Sentimen Review Film Berbahasa Inggris Dengan Pendekatan Bidirectional Encoder Representations from Transformers." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 6, no. 2 (2020): 181–93. http://dx.doi.org/10.35957/jatisi.v6i2.206.

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Sentimen Analisis adalah proses analisis terhadap suatu pendapat atau sikap seseorang. Sentimen analisis digunakan untuk mendapatkan suatu hasil analisa terhadap berbagai macam pendapat atau sikap seseorang dalam memberikan komentar atau opininya. Pada penelitian ini, penulis melakukan klasifikasi sentiment analysis terhadap review film dengan menggunakan Dataset cornell edu dari pabo untuk movie review dengan proses klasifikasi menggunakan algoritma Bidirectional Encoder Representations from Transformers (BERT) yang dilakukan fine tuning dengan beberapa layer untuk klasifikasi. Dari penelitia

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Shi, Yongqi, Ruopeng Yang, Changsheng Yin, Yiwei Lu, Yuantao Yang, and Yu Tao. "Entity Linking Method for Chinese Short Texts with Multiple Embedded Representations." Electronics 12, no. 12 (2023): 2692. http://dx.doi.org/10.3390/electronics12122692.

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Entity linking, a crucial task in the realm of natural language processing, aims to link entity mentions in a text to their corresponding entities in the knowledge base. While long documents provide abundant contextual information, facilitating feature extraction for entity identification and disambiguation, entity linking in Chinese short texts presents significant challenges. This study introduces an innovative approach to entity linking within Chinese short texts, combining multiple embedding representations. It integrates embedding representations from both entities and relations in the kn

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Jeet Rawat, Amar, Sunil Ghildiyal, and Anil Kumar Dixit. "Topic modelling of legal documents using NLP and bidirectional encoder representations from transformers." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1749. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1749-1755.

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<span>Modeling legal text is a difficult task because of its unique features, such as lengthy texts, complex language structures, and technical terms. During the last decade, there has been a big rise in the number of legislative documents, which makes it hard for law professionals to keep up with legislation like analyzing judgements and implementing acts. The relevancy of topics is heavily influenced by the processing and presentation of legal documents in some contexts. The objective of this work is to understand the legal judgement corpus related to cases under the Hindu Marriage Act

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Fatonah, Fany Risti, Dian Sa'adillah Maylawati, and Eva Nurlatifah. "Chatbot Edukasi Pra-Nikah berbasis Telegram Menggunakan Bidirectional Encoder Representations From Transformers (BERT)." Jurnal Algoritma 21, no. 2 (2024): 29–40. https://doi.org/10.33364/algoritma/v.21-2.1657.

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Tingginya angka perceraian dan penurunan minat untuk menikah di Indonesia memunculkan kebutuhan akan pendekatan baru dalam edukasi pranikah. Dengan memanfaatkan teknologi Natural Language Processing, penelitian ini bertujuan untuk mengembangkan mesih chatbot menjadi solusi dalam edukasi pre-nikah yang dengan memberikan informasi efektif dan efisien kepada pasangan calon pengantin secara realtime. Penelitian ini menggunakan model Bidirectional Encoder Representations from Transformers (BERT) dengan chatbot berupa konteks dari website Kementerian Agama dan buku edukasi pernikahan. Model ini diim

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Areshey, Ali, and Hassan Mathkour. "Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model." Sensors 23, no. 11 (2023): 5232. http://dx.doi.org/10.3390/s23115232.

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Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose of sentiment analysis is to determine an author’s attitude toward a subject or the overall tone of a document. There is a huge collection of studies that make an effort to predict how useful online reviews will be and have produced conflicting results on the efficacy of different methodologies. Furthermore, many of the current solutions employ manu

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Ilham, Fikri, and Warih Maharani. "Analyze Detection Depression In Social Media Twitter Using Bidirectional Encoder Representations from Transformers." Journal of Information System Research (JOSH) 3, no. 4 (2022): 476–82. http://dx.doi.org/10.47065/josh.v3i4.1885.

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Human health is an essential part of the welfare of a country. Early detection of a disease is necessary to prevent it from spreading in an area. Social media is now a rapid and widespread development of information to provide convenience for the public to communicate. Depressed people have a variety of depressive symptoms from every human behaviour. Psychological doctors often conduct face-to-face interviews on commonly used diagnoses and statistical manual criteria for mental disorders. Depression is a mental disorder that typically appears in humans with the characteristics of depressed moo

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Rawat, Amar Jeet, Sunil Ghildiyal, and Anil Kumar Dixit. "Topic modelling of legal documents using NLP and bidirectional encoder representations from transformers." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1749–55. https://doi.org/10.11591/ijeecs.v28.i3.pp1749-1755.

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Modeling legal text is a difficult task because of its unique features, such as lengthy texts, complex language structures, and technical terms. During the last decade, there has been a big rise in the number of legislative documents, which makes it hard for law professionals to keep up with legislation like analyzing judgements and implementing acts. The relevancy of topics is heavily influenced by the processing and presentation of legal documents in some contexts. The objective of this work is to understand the legal judgement corpus related to cases under the Hindu Marriage Act of India. T

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