Information Theory Quantifiers in Cryptocurrency Time Series Analysis by Micaela Suriano, Leonidas Facundo Caram, Cesar Caiafa, Hernán Daniel Merlino, Osvaldo Anibal Rosso

“…Additionally, Natural Language Processing (NLP) analysis identified the most relevant terms in each white paper, facilitating a clustering method that resulted in four distinct clusters. …”
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Automated Microservices Identification Through Business Process Analysis: A Semantic-Driven Clustering Approach by Idris Oumoussa, Rajaa Saidi

“…Addressing limitations in existing microservices identification techniques, this approach utilizes advanced Natural Language Processing (NLP) techniques—such as Named Entity Recognition (NER) and semantic analysis—to capture dependencies and align boundaries with business logic. …”
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Specialization of the brain for language in children with Fragile X Syndrome: a functional Near Infrared Spectroscopy study by Elizabeth Smith, Kelli C. Dominick, Lauren M. Schmitt, Ernest V. Pedapati, Craig A. Erickson

“…Fragile X Syndrome (FXS) is a neurogenetic disorder marked by high rates of delays in both expressive and receptive language, but neural activation patterns during speech and language processing are unknown. We report results of a functional Near Infrared Spectroscopy (fNIRS) study of responses to speech and nonspeech sounds in the auditory cortex in a sample of 2- to 10-year-old children with FXS and typically developing controls (FXS n = 23, TDC n = 15, mean age = 6.44 and 7.07 years, respectively). …”
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An NLP-based novel approach for assessing national influence in clause dissemination across bilateral investment treaties. by Shahadat Uddin, Haohui Lu, Wolfgang Alschner, Dori Patay, Nicholas Frank, Fabio S Gomes, Anne Marie Thow

“…Following an NLP (Natural Language Processing)-based text similarity analysis of Bilateral Investment Treaties (BITs), this study proposes a metric, 'Influence' (INF), which provides insights into the role of different countries or regions in the propagation of IIA texts among BITs. …”
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Customers' sentiment on food delivery services: An Arabic text mining approach by Dheya Mustafa, Safaa M. Khabour, Ahmed S. Shatnawi

“…The present study collects FDS-related reviews in Arabic to conduct extensive emotion mining, taking advantage of Natural Language Processing, feature selection, and Machine Learning techniques to elicit personal judgments, identify polarity, and recognize customers’ feelings in the FDS domain. …”
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Big Data Meets Social Networks: A Survey of Analytical Strategies and Research Challenges by Shashank Sheshar Singh, Shashank Singh, Kuldeep Singh, Vishal Srivastava, Harish Kumar Shakya

“…The survey covers a wide range of topics, including methods for pre-processing and cleaning social network data, as well as more advanced analytical approaches such as opinion mining, sentiment analysis, text mining, and natural language processing. It also discusses various big data architectures for SN analytics, like Lambda architecture, Kappa architecture, and GraphX architecture. …”
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A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods by Fatima Rashed Alzaabi, Abid Mehmood

“…Furthermore, the survey explores the utilization of modern deep learning and natural language processing (NLP) based methods as promising alternatives, shedding light on their advantages over traditional methods. …”
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MAHGA: Multi-Aspect Heterogeneous Graph Analysis for Harmful Speech Detection on Social Networks by Ryo Yoshida, Soh Yoshida, Mitsuji Muneyasu

“…Graph-based methods, particularly those employing graph neural networks, effectively capture these contextual interactions by modeling relationships within heterogeneous graphs and integrate diverse semantic elements derived through natural language processing. However, existing graph approaches do not adequately differentiate between explicit textual elements (e.g., hashtags, mentions), implicit semantic elements (e.g., entities, topics), and informational or emotional cues from images, thereby obscuring the distinct contribution of each component. …”
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T-LLaMA: a Tibetan large language model based on LLaMA2 by Hui Lv, Chi Pu, La Duo, Yan Li, Qingguo Zhou, Jun Shen

“…To our knowledge, T-LLaMA stands as the first large-scale language model in Tibetan natural language processing (NLP) with parameters in the billion range. …”
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The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity by Lauren E Walker, Aseel S Abuzour, Danushka Bollegala, Andrew Clegg, Mark Gabbay, Alan Griffiths, Cecil Kullu, Gary Leeming, Frances S Mair, Simon Maskell, Samuel Relton, Roy A Ruddle, Eduard Shantsila, Matthew Sperrin, Tjeerd Van Staa, Alan Woodall, Iain Buchan

“…Structured clinical data will be drawn from integrated care records (general practice, hospital, and social care) covering an ∼11m population supplemented with Natural Language Processing (NLP) of unstructured clinical text. …”
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Advanced Hyena Hierarchy Architectures for Predictive Modeling of Interest Rate Dynamics from Central Bank Communications by Tao Song, Shijie Yuan, Rui Zhong

“…However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of thousands of tokens. …”
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Positive mood enhances gender stereotype activation during semantic integration and re-analysis by Marcin Naranowicz, Katarzyna Jankowiak

“…Early research also highlights the role of colour–gender associations in language processing. However, the neurocognitive mechanisms underlying the interaction between mood, gender stereotype activation, and colour–gender associations remain underexplored. …”
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Text leads, images and videos follow: the impact of processing paths and input modalities on metaphorical competence in Chinese foreign language learners by Siyun Li, Yang Zhao

“…IntroductionMetaphorical competence is crucial in the accurate and authentic use of a second language. Processing paths (rational processing verses contextual guessing) and input modalities (unimodal verses multimodal) are major factors that may influence learners' metaphorical acquisition. …”
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STSG: A Short Text Semantic Graph Model for Similarity Computing Based on Dependency Parsing and Pre-trained Language Models by Hai Liao, Yan Liang, Song Chen, Lingyun Xiang, Zhimin Chang, Yun Xiao

“…Short text semantic similarity is a crucial research area in nature language processing, which is used to predict the similarity between two sentences. …”
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Statistical analysis of Hindi multi-word expressions using multiple threshold method by Rakhi Joon, Archana Singhal, Nupur Chugh, Gargi Mishra

“…MWEs play a vital role in many Natural Language Processing (NLP) applications like machine translation, information retrieval, text processing, and other practical applications. …”
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PolyReg: Autoregressive Building Outline Regularization via Masked Attention Sequence Generation by Longfei Cui, Chao Li, Xin Chen, Xiao Wang, Haizhong Qian

“…The PolyReg model draws on the sequence generation idea from natural language processing, transforming the outline regularization task into a sequence generation problem. …”
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Deteksi Cyberbullying dengan Mesin Pembelajaran Klasifikasi (Supervised Learning): Peluang dan Tantangan by Yudi Setiawan, Nur Ulfa Maulidevi, Kridanto Surendro

“…Penelitian deteksi pola dan karakteristik cyberbullying banyak dilakukan dengan berbagai metode, seperti dengan mengimplementasikan Machine Learning, Natural Language Processing (NLP), dan Sentiment Analysis yang memiliki variasi akurasi yang berbeda, dengan keunggulan dan kelemahan dari masing-masing metode. …”
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Investigating banks’ social media content and consumer reactions with machine learning by Gombos Nóra, Julianna, Vlaszov Artúr, Bíró-Szigeti Szilvia, Molontay Roland

“…Design/methodology/approach: – Text and image analysis of the Twitter accounts available to the public was performed using different natural language processing methods (topic analysis, sentiment analysis), computer vision techniques (object detection, optical character recognition, image captioning), and statistical and machine learning models (linear and logistic regression, Catboost). …”
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A Hybrid Neuro-Symbolic Pipeline for Coreference Resolution and AMR-Based Semantic Parsing by Christos Papakostas, Christos Troussas, Akrivi Krouska, Cleo Sgouropoulou

“…Large Language Models (LLMs) have transformed Natural Language Processing (NLP), yet they continue to struggle with deep semantic understanding, particularly in tasks like coreference resolution and structured semantic inference. …”
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Assessing the Intellectual Structure of the Evolving Knowledge Base on ChatGPT in the Field of Education and Health by Murat Demirkol and Nedim Malkoc

“…During the second period, though, the studies featured themes that are closely related to the design and performance of ChatGPT such as large language models (LLMs), natural language processing (NLP) and chatbot while abandoning a focus on artificial intelligence. …”
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