A comprehensive dataset and neural network approach for named entity recognition in the Uzbek languageMendeley Data by Davlatyor Mengliev, Vladimir Barakhnin, Mukhriddin Eshkulov, Bahodir Ibragimov, Shohrux Madirimov

“…The proposed dataset and trained model contribute to the development of natural language processing in the Uzbek language. In addition, the authors also conducted an analysis of existing works, as well as a comparative analysis, which will help to identify the distinctive features and novelty of the proposed work. …”
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What do pseudowords tell us about word processing? An overview by Celia Martínez-Tomás, Celia Martínez-Tomás, Ana Baciero, Ana Baciero, Miguel Lázaro, José A. Hinojosa, José A. Hinojosa, José A. Hinojosa

“…By providing a focused synthesis of empirical findings, this article underscores the critical insights that research using pseudowords offers into the interconnected nature of cognitive mechanisms in language processing.…”
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TIBW: Task-Independent Backdoor Watermarking with Fine-Tuning Resilience for Pre-Trained Language Models by Weichuan Mo, Kongyang Chen, Yatie Xiao

“…Pre-trained language models such as BERT, GPT-3, and T5 have made significant advancements in natural language processing (NLP). However, their widespread adoption raises concerns about intellectual property (IP) protection, as unauthorized use can undermine innovation. …”
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EEG reveals brain network alterations in chronic aphasia during natural speech listening by Ramtin Mehraram, Jill Kries, Pieter De Clercq, Maaike Vandermosten, Tom Francart

“…Abstract Aphasia is a common consequence of a stroke which affects language processing. In search of an objective biomarker for aphasia, we used EEG to investigate how functional network patterns in the cortex are affected in persons with post-stroke chronic aphasia (PWA) compared to healthy controls (HC) while they are listening to a story. …”
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A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit by Edwin F. Juarez, Bennet Peterson, Erica Sanford Kobayashi, Sheldon Gilmer, Laura E. Tobin, Brandan Schultz, Jerica Lenberg, Jeanne Carroll, Shiyu Bai-Tong, Nathaly M. Sweeney, Curtis Beebe, Lawrence Stewart, Lauren Olsen, Julie Reinke, Elizabeth A. Kiernan, Rebecca Reimers, Kristen Wigby, Chris Tackaberry, Mark Yandell, Charlotte Hobbs, Matthew N. Bainbridge

“…Abstract The Mendelian Phenotype Search Engine (MPSE), a clinical decision support tool using Natural Language Processing and Machine Learning, helped neonatologists expedite decisions to whole genome sequencing (WGS) to diagnose patients in the neonatal intensive care unit. …”
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Predicting accrual success for better clinical trial resource allocation by Sisi Ma, Yinzhao Wang, John Wagner, Steve Johnson, Serguei Pakhomov, Constantin Aliferis

“…Features of the dataset were informed by prior literature and constructed using data-driven natural language processing methods. We built predictive models for accrual failure using state-of-the-art supervised machine learning protocols and methods. …”
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Hate Speech Detection Using Large Language Models: A Comprehensive Review by Aish Albladi, Minarul Islam, Amit Das, Maryam Bigonah, Zheng Zhang, Fatemeh Jamshidi, Mostafa Rahgouy, Nilanjana Raychawdhary, Daniela Marghitu, Cheryl Seals

“…We analyze the evolution of LLMs in natural language processing and examine their strengths and limitations in identifying hate speech. …”
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CTHP: Selection for adoption of open‐source bioinformatics software based on a customised ISO 25010 quality model, three‐way decision and Delphi hierarchy process by Yuqi Li, Yixin Bian, Ziheng Zhang, Song Zhao, Yiqi Liu

“…Finally, as a case study, the proposed approach is applied to assist the decision‐making process of two popular natural language processing frameworks in the Bioinformatics area. …”
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The Utility of ChatGPT in Educational Research—Potential Opportunities and Pitfalls by Turgut Karakose

“…Background/purpose –Leveraging the latest developments in natural language processing, recent versions of ChatGPT has demonstrated astounding performance on a wide array of tasks through its ability to understand and generate human language, and respond to diverse conversational prompts. …”
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A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity by Abdullah Al Siam, Moutaz Alazab, Albara Awajan, Nuruzzaman Faruqui

“…We examine cutting-edge AI methodologies and principal models across many domains, including machine learning algorithms, deep learning architectures, natural language processing techniques, and anomaly detection algorithms, emphasizing their distinct contributions to enhancing security. …”
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Toward the Development of Large-Scale Word Embedding for Low-Resourced Language by Shahzad Nazir, Muhammad Asif, Shahbaz Ahmad Sahi, Shahbaz Ahmad, Yazeed Yasin Ghadi, Muhammad Haris Aziz

“…Word embedding is possessed by Natural language processing as a key procedure for semantically and syntactically manipulating the unlabeled text corpus. …”
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Larger models yield better results? Streamlined severity classification of ADHD-related concerns using BERT-based knowledge distillation. by Ahmed Akib Jawad Karim, Kazi Hafiz Md Asad, Md Golam Rabiul Alam

“…This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT-based model for natural language processing (NLP) applications. After the model creation, we applied the resulting model, LastBERT, to a real-world task-classifying severity levels of Attention Deficit Hyperactivity Disorder (ADHD)-related concerns from social media text data. …”
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Time Course of Brain Activity Changes Related to Number (Quantity) Processing Triggered by Digits Versus Number Words: An Event-Related Potential (ERP) Study by Peter Walla, Philipp Klimovic

“…The neuroscience of language processing in the human brain has a long history. …”
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Improving Sentiment Analysis Performance on Imbalanced Moroccan Dialect Datasets Using Resample and Feature Extraction Techniques by Zineb Nassr, Faouzia Benabbou, Nawal Sael, Touria Hamim

“…Sentiment analysis is a crucial component of text mining and natural language processing (NLP), involving the evaluation and classification of text data based on its emotional tone, typically categorized as positive, negative, or neutral. …”
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Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review by Dipak Gautam, Philipp Kellmeyer

“… BackgroundThe rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. …”
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A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network by Shuang Liu, Zeng Zhuang, Yanfeng Zheng, Simon Kolmanic

“…To address this problem, the Transformer from the field of natural language processing (NLP) was introduced to the image segmentation field. …”
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Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning by Jingteng Li, Kimberley R. Zakka, John Booth, Louise Rigny, Samiran Ray, Mario Cortina-Borja, Payam Barnaghi, Neil Sebire

“…Abstract Introduction Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR). …”
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Supervised learning and resampling techniques on DISC personality classification using Twitter information in Bahasa Indonesia by Ema Utami, Irwan Oyong, Suwanto Raharjo, Anggit Dwi Hartanto, Sumarni Adi

“…A pure natural language processing (NLP) approach can give decent results, and its reliability can be improved by combining it with machine learning (as shown by previous studies). …”
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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…Transformers are becoming dominant deep learning backbones for both computer vision and natural language processing. While extensive experiments prove its outstanding ability for large models, transformers with small sizes are not comparable with convolutional neural networks in various downstream tasks due to its lack of inductive bias which can benefit image understanding. …”
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Using machine learning-based models for personality recognition by Fatemeh Mohades Deilami, Hossein Sadr, Mozhdeh Nazari

“…Among various deep neural networks, Convolutional Neural Networks (CNN) have demonstrated profound efficiency in natural language processing and especially personality detection. …”
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