Being vulnerable with viewers: Exploring how medical YouTubers communicated about COVID-19 with the public. by Seung Woo Chae, Noriko Hara, Harshit Rakesh Shiroiya, Janice Chen, Ellen Ogihara

“…We employed natural language processing to analyze the linguistic and emotional dimensions of these two text sets including analytical thinking, positive emotion, and negative emotion, the last of which was divided into anxiety, anger, and sadness. …”
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Towards cross-platform interoperability for machine-assisted text annotation by Richard Eckart de Castilho, Nancy Ide, Jin-Dong Kim, Jan-Christoph Klie, Keith Suderman

“…In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seamless communication across different platforms. …”
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Comparing a Thai Words Segmentation Methods in the LST20 Dataset by Krittapol Damrongkamoltip, Khatcha Ruenlek, Wasit Limprasert, Prachya Boonkwan

“…Although data is easily available, there are still challenges in natural language processing tasks, especially, the division of Thai words that lacks clarity of word boundaries, etc. …”
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Schizophrenia more employable than depression? Language-based artificial intelligence model ratings for employability of psychiatric diagnoses and somatic and healthy controls. by Maximin Lange, Alexandros Koliousis, Feras Fayez, Eoin Gogarty, Ricardo Twumasi

“…Hence occupational and mental health bias in existing Natural Language Processing (NLP) models used in recruiting and job hunting must be assessed. …”
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Artificial intelligence across oncology specialties: current applications and emerging tools by Frank Lin, Tim Rattay, John Kang, Evangelia Katsoulakis, Kyle Lafata, Ellen Kim, Christopher Yao, Harsha Nori, Christoph Ilsuk Lee

“…In this review, we provide multidisciplinary perspectives on oncology applications touched by AI—imaging, pathology, patient triage, radiotherapy, genomics-driven therapy and surgery—and integration with existing tools—natural language processing, digital twins and clinical informatics.…”
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Are queries and keys always relevant? A case study on transformer wave functions by Riccardo Rende, Luciano Loris Viteritti

“…The dot product attention mechanism, originally designed for natural language processing tasks, is a cornerstone of modern Transformers. …”
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A Bibliometric Perspective on AI Research for Job-Résumé Matching by Sergio Rojas-Galeano, Jorge Posada, Esteban Ordoñez

“…Recent advances in AI, specifically in the fields of text analytics and natural language processing, have sparked the interest of research on the application of these technologies to help recruiters accomplish this task or part of it automatically, applying algorithms for information extraction, parsing, representation, and matching of résumés and job descriptions, or sections within. …”
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Novel defense based on softmax activation transformation by Jinyin CHEN, Changan WU, Haibin ZHENG

“…Deep learning is widely used in various fields such as image processing, natural language processing, network mining and so on.However, it is vulnerable to malicious adversarial attacks and many defensive methods have been proposed accordingly.Most defense methods are attack-dependent and require defenders to generate massive adversarial examples in advance.The defense cost is high and it is difficult to resist black-box attacks.Some of these defenses even affect the recognition of normal examples.In addition, the current defense methods are mostly empirical, without certifiable theoretical support.Softmax activation transformation (SAT) was proposed in this paper, which was a light-weight and fast defense scheme against black-box attacks.SAT reactivates the output probability of the target model in the testing phase, and then it guarantees privacy of the probability information.As an attack-free defense, SAT not only avoids the burden of generating massive adversarial examples, but also realizes the advance defense of attacks.The activation of SAT is monotonic, so it will not affect the recognition of normal examples.During the activation process, a variable privacy protection transformation coefficient was designed to achieve dynamic defense.Above all, SAT is a certifiable defense that can derive the effectiveness and reliability of its defense based on softmax activation transformation.To evaluate the effectiveness of SAT, defense experiments against 9 attacks on MNIST, CIFAR10 and ImageNet datasets were conducted, and the average attack success rate was reduced from 87.06% to 5.94%.…”
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Detecting privacy compliance of mobile applications from the perspective of the "minimum necessary" principle by YU Peihou, XU Tianchen, SUN Wenqian, CHEN Yunfang, YU Le, ZHANG Wei

“…Researchers have proposed various methods using natural language processing (NLP) techniques to analyze privacy policy texts and perform compliance checks. …”
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Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping by Dino Dobrinić, Mario Miler, Damir Medak

“…., screening) by using natural language processing and large language models. In total, this review analyzed 55 papers that included keywords related to GI mapping and provided materials and learning methods (i.e., machine or deep learning) essential for effective green infrastructure mapping. …”
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Evaluation of Different Stemming Techniques on Arabic Customer Reviews by Hawraa Fadhil Khelil, Mohammed Fadhil Ibrahim, Hafsa Ataallah Hussein, Raed Kamil Naser

“…Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. …”
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GDPR-oriented intelligent checking method of privacy policies compliance by Xin LI, Peng TANG, Xiheng ZHANG, Weidong QIU, Hong HUI

“…The implementation of the EU’s General Data Protection Regulation (GDPR) has resulted in the imposition of over 300 fines since its inception in 2018.These fines include significant penalties for prominent companies like Google, which were penalized for their failure to provide transparent and comprehensible privacy policies.The GDPR, known as the strictest data protection laws in history, has made companies worldwide more cautious when offering cross-border services, particularly to the European Union.The regulation's territorial scope stipulates that it applies to any company providing services to EU citizens, irrespective of their location.This implies that companies worldwide, including domestic enterprises, are required to ensure compliance with GDPR in their privacy policies, especially those involved in international operations.To meet this requirement, an intelligent detection method was introduced.Machine learning and automation technologies were utilized to automatically extract privacy policies from online service companies.The policies were converted into a standardized format with a hierarchical structure.Through natural language processing, the privacy policies were classified, allowing for the identification of relevant GDPR concepts.In addition, a constructed GDPR taxonomy was used in the detection mechanism to identify any missing concepts as required by GDPR.This approach facilitated intelligent detection of GDPR-oriented privacy policy compliance, providing support to domestic enterprises while they provided cross-border services to EU users.Analysis of the corpus samples reveals the current situation that mainstream online service companies generally fail to meet GDPR compliance requirements.…”
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Annotated corpus for traditional formula-disease relationships in biomedical articles by Sangjun Yea, Ho Jang, Soyoung Kim, Sanghun Lee, Jaeuk U. Kim

“…A significant portion of the state-of-the-art knowledge regarding the relationship between TF and disease is found in scientific publications, where manual knowledge extraction is impractical. Thus, Natural Language Processing (NLP) is being employed to efficiently and accurately search and extract crucial knowledge from unstructured literatures. …”
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MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion by Xuming Han, Feng Zhou, Zhiyuan Hao, Qiaoming Liu, Yong Li, Qi Qin

“…Named entity recognition (NER) is a subtask in natural language processing, and its accuracy greatly affects the effectiveness of downstream tasks. …”
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Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based Models by Tahira Amin, Zahoor-Ur-Rehman Tanoli, Farhan Aadil, Khalid Mahmood Awan, Sangsoon Lim

“…Despite the impressive capabilities of generalized transformer models in various natural language processing (NLP) domains, their application to essay scoring has often fallen short of expectations. …”
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Crowdsourcing geographic information for terrorism-related disaster awareness and mitigation: perspectives and challenges by Michaelmary Chukwu, Xiao Huang, Siqin Wang, Di Yang, Xinyue Ye

“…Despite the prevalence of natural language processing for data mining, the majority of studies did not incorporate ML algorithms in their analyses. …”
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Research on the robustness of neural machine translation systems in word order perturbation by Yuran ZHAO, Tang XUE, Gongshen LIU

“…Pre-trained language model is one of the most important models in the natural language processing field, as pre-train-finetune has become the paradigm in various NLP downstream tasks.Previous studies have proved integrating pre-trained language models (e.g., BERT) into neural machine translation (NMT) models can improve translation performance.However, it is still unclear whether these improvements stem from enhanced semantic or syntactic modeling capabilities, as well as how pre-trained knowledge impacts the robustness of the models.To address these questions, a systematic study was conducted to examine the syntactic ability of BERT-enhanced NMT models using probing tasks.The study revealed that the enhanced models showed proficiency in modeling word order, highlighting their syntactic modeling capabilities.In addition, an attacking method was proposed to evaluate the robustness of NMT models in handling word order.BERT-enhanced NMT models yielded better translation performance in most of the tasks, indicating that BERT can improve the robustness of NMT models.It was observed that BERT-enhanced NMT model generated poorer translations than vanilla NMT model after attacking in the English-German translation task, which meant that English BERT worsened model robustness in such a scenario.Further analyses revealed that English BERT failed to bridge the semantic gap between the original and perturbed sources, leading to more copying errors and errors in translating low-frequency words.These findings suggest that the benefits of pre-training may not always be consistent in downstream tasks, and careful consideration should be given to its usage.…”
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Vision Transformers for Image Classification: A Comparative Survey by Yaoli Wang, Yaojun Deng, Yuanjin Zheng, Pratik Chattopadhyay, Lipo Wang

“…Transformers were initially introduced for natural language processing, leveraging the self-attention mechanism. …”
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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. …”
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Hyperbolic Graph Convolutional Network Relation Extraction Model Combining Dependency Syntax and Contrastive Learning by Jinzhe Li

“…However, most studies are affected by the noise in the syntactic information automatically extracted by natural language processing toolkits. Additionally, traditional pre-training encoders have issues such as an overly centralized representation of word embedding for high-frequency words, which adversely affects the model to learn contextual semantic information. …”
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