Recommendations on Informational Monitoring of the Safety and Efficacy of Medicinal Products in the Russian Federation as Part of Pharmacovigilance by K. S. Milchakov

“…It describes modern technological solutions in the field of information monitoring, substantiating the suitability of new achievements in such areas as Data Science and natural language processing (NLP) for marketing authorisation holders to collect and analyse data on the safety and efficacy of medicinal products. …”
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Enhancing smart contract security: Leveraging pre‐trained language models for advanced vulnerability detection by Fei He, Fei Li, Peili Liang

“…This research not only offers a powerful tool to bolster smart contract security, mitigating financial risks for average users, but also serves as a valuable reference for advancements in natural language processing and deep learning.…”
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Lightweight Morphological Analysis Model for Smart Home Applications Based on Natural Language Interfaces by Sangwoo Kang, Harksoo Kim, Hyun-Kyu Kang, Jungyun Seo

“…With the rapid evolution of the smart home environment, the demand for natural language processing (NLP) applications on information appliances is increasing. …”
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The Semantics and Collocations Relation in Food Reviews by Fazel Keshtkar, Ledong Shi, Syed Ahmad Chan Bukhari

“…The purpose of this study is to use computational linguistics and natural language processing to categorise and find semantic relation in various dishes based on reviewers’ comments and menus description. …”
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Enhancing zero-shot relation extraction with a dual contrastive learning framework and a cross-attention module by Diyou Li, Lijuan Zhang, Jie Huang, Neal Xiong, Lei Zhang, Jian Wan

“…Abstract Zero-shot relation extraction (ZSRE) is essential for improving the understanding of natural language relations and enhancing the accuracy and efficiency of natural language processing methods in practical applications. However, the existing ZSRE models ignore the importance of semantic information fusion and possess limitations when used for zero-shot relation extraction tasks. …”
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ENHANCING NAMED ENTITY RECOGNITION ON HINER DATASET USING ADVANCED NLP TECHNIQUES by Harshvardhan Pardeshi, Prof. Piyush Pratap Singh

“… Named entity recognition (NER) is a natural language processing (NLP) categorization labelling job where the objective is to assign words to a predefined set of named entity classes. …”
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Specificity of the Analysis of Syrian Arabic Words and Expressions by Aida D. Haddad, Natalia V. Novospasskaya

“…The result of this work is an algorithm developed by the authors, aimed at improving natural language processing, for analyzing dialectal lexical units and expressions in the Syrian dialect of the Arabic, allowing for the acquisition of the most complete information about their origins, phonetic and grammatical features.…”
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Evolutionary game analysis of stakeholder privacy management in the AIGC model by Yali Lv, Jian Yang, Xiaoning Sun, Huafei Wu

“…The technological development powered by Artificial Intelligence Generated Content (AIGC) models, exemplified by Generative Pre-trained Transformer 4 (GPT-4) and Bidirectional Encoder Representations from Transformers (BERT), has completely transformed machine language processing and fostered substantial technological advancements. …”
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Building an end-to-end battery recipe knowledge base via transformer-based text mining by Daeun Lee, Hiroshi Mizuseki, Jaewoong Choi, Byungju Lee

“…Abstract Recent studies have increasingly applied natural language processing to automatically extract experimental information from battery materials literature. …”
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Leveraging Large Language Models in Tourism: A Comparative Study of the Latest GPT Omni Models and BERT NLP for Customer Review Classification and Sentiment Analysis by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas, Dimitrios K. Nasiopoulos

“…This study undertakes a comparative analysis of traditional natural language processing (NLP) models, such as BERT and advanced large language models (LLMs), specifically GPT-4 omni and GPT-4o mini, both pre- and post-fine-tuning with few-shot learning. …”
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Exploring Large Language Models and the Metaverse for Urologic Applications: Potential, Challenges, and the Path Forward by Hyung Jun Park, Eun Joung Kim, Jung Yoon Kim

“…Concurrently, generative artificial intelligence, especially large language models, is being integrated into healthcare for applications in data analysis, image recognition, and natural language processing. In urology, large language models (LLMs) support are increasingly used in urology for tasks such as image diagnosis, data processing, patient education, and treatment assistance in order to provide significant support in clinical settings. …”
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Bilingual Dialogue Dataset with Personality and Emotion Annotations for Personality Recognition in Education by Zhi Liu, Yao Xiao, Zhu Su, Luyao Ye, Kaili Lu, Xian Peng

“…Abstract Dialogue datasets are essential for advancing natural language processing (NLP) tasks. However, many existing datasets lack integrated annotations for personality and emotion, limiting models’ ability to effectively capture these aspects and generate personalized, human-like dialogues, which ultimately impact user experience. …”
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Earthwork Network Architecture (ENA): Research for Earthwork Quantity Estimation Method Improvement with Large Language Model by Taewook Kang, Kyubyung Kang

“…These findings suggest that LLMs, typically used in natural language processing, can be effectively adapted for complex AEC datasets. …”
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Using Graph-Based Maximum Independent Sets with Large Language Models for Extractive Text Summarization by Cengiz Hark

“…The framework encapsulates nodes and relations into structural graphs generated through Natural Language Processing (NLP) techniques based on the Maximum Independent Set (MIS) theory. …”
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Chinese Legal Case Similarity Matching Based on Text Importance Extraction by Aman Fan, Shaoxi Wang, Yanchuan Wang

“…Recent advances in natural language processing (NLP), particularly those based on deep learning technologies, have significantly enhanced the intelligent development of similar case judgments. …”
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A novel sub-network level ensemble deep neural network with a regularized loss function to improve prediction performance by Jalil Toosifar, Yahya Forghani, Seyyed Abed Hosseini, Nasser Shoeibi

“…Abstract Deep neural networks have been widely applied in various fields—such as image recognition, natural language processing, and robotics—achieving remarkable success. …”
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An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization by Chenhao Li, Jiyin Zhang, Weilin Chen, Xiaogang Ma

“…To address this issue, we propose a three-layer ranking system that integrates natural language processing and machine learning techniques for relevance and trust assessment. …”
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OPTIMIZING LONG TEXT CLASSIFICATION PERFORMANCE THROUGH KEYWORD-BASED SENTENCE SELECTION: A CASE STUDY ON ONLINE NEWS CLASSIFICATION FOR INDONESIAN GDP GROWTH-RATE DETECTION by Dinda Pusparahmi Sholawatunnisa, Lya Hulliyyatus Suadaa

“…This study delves into innovative approaches to streamline the Gross Domestic Product (GDP) computation process by harnessing modern data analytics, Natural Language Processing (NLP), and online news sources. Leveraging online news data introduces real-time information, promising to improve the accuracy and timeliness of economic indicators like GDP. …”
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Graph Neural Networks: Architectures, Applications, and Future Directions by Valerio Ponzi, Christian Napoli

“…In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. …”
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Optimizing Convolutional Neural Network Architectures by Luis Balderas, Miguel Lastra, José M. Benítez

“…Convolutional neural networks (CNNs) are commonly employed for demanding applications, such as speech recognition, natural language processing, and computer vision. As CNN architectures become more complex, their computational demands grow, leading to substantial energy consumption and complicating their use on devices with limited resources (e.g., edge devices). …”
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