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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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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Applications of Artificial Intelligence in Ophthalmology: General Overview by Wei Lu, Yan Tong, Yue Yu, Yiqiao Xing, Changzheng Chen, Yin Shen

“…With the emergence of unmanned plane, autonomous vehicles, face recognition, and language processing, the artificial intelligence (AI) has remarkably revolutionized our lifestyle. …”
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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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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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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. 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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AzSLD: Azerbaijani sign language dataset for fingerspelling, word, and sentence translation with baseline softwareZenodo by Nigar Alishzade, Jamaladdin Hasanov

“…Advancements in sign language processing technology hinge on the availability of extensive, reliable datasets, comprehensive instructions, and adherence to ethical guidelines. …”
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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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Low-Resource Active Learning of Morphological Segmentation by Stig-Arne Grönroos, Katri Hiovain, Peter Smit, Ilona Rauhala, Kristiina Jokinen, Mikko Kurimo, Sami Virpioja

“… Many Uralic languages have a rich morphological structure, but lack morphological analysis tools needed for efficient language processing. While creating a high-quality morphological analyzer requires a significant amount of expert labor, data-driven approaches may provide sufficient quality for many applications. …”
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The Challenges of Gender Diversity in Boards of Directors: An Australian Study with Global Implications by Suzanne Young, Karen Farquharson, Daswin De Silva, Paul Mather

“…In‐depth interviews are conducted of those with first‐hand experience of board appointments, followed by the thematic analysis and the application of natural language processing techniques to identify emotions and sentiment associated with these themes. …”
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Neuroanatomy, episodic memory and inhibitory control of Persian-Kurdish simultaneous bilinguals by Samira Golshani, Olga Kepinska, Hamid Gholami, Narly Golestani

“…Abstract We assessed simultaneous bilinguals and monolinguals on inhibitory control and episodic memory, and assessed their grey matter volumes in brain regions known to be involved in language processing, executive control and memory. Bilinguals outperformed monolinguals on episodic memory, and performance on the memory and inhibition tasks were correlated, only in the bilingual group. …”
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Data Augmentation For Sorani Kurdish News Headline Classification Using Back-Translation And Deep Learning Model by Soran Badawi

“…The findings suggest that the combination of back-translation and a proposed BiLSTM model is a promising approach for data augmentation in low-resource languages, contributing to the advancement of natural language processing in under-resourced languages. Moreover, having a Kurdish news headline classification model can improve access to news and information for Kurdish speakers. …”
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Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree by Nasir Tayarani, Mehrdad Jalali

“…Text mining, as a special strategy, drives the knowledge discovery process, which uses non-verbal and attractive patterns of natural language processing. In this paper, a new hybrid approach of machine learning and vocabulary-based method to text-mining sentiment analysis on Twitter. …”
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An End-to-End Rumor Detection Model Based on Feature Aggregation by Aoshuang Ye, Lina Wang, Run Wang, Wenqi Wang, Jianpeng Ke, Danlei Wang

“…Furthermore, the features used by the deep learning method based on natural language processing are heavily limited. Therefore, it is of great significance and practical value to study the rumor detection method independent of feature engineering and effectively aggregate heterogeneous features to adapt to the complex and variable social network. …”
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A knowledge graph for crop diseases and pests in China by Rongen Yan, Ping An, Xianghao Meng, Yakun Li, Dongmei Li, Fu Xu, Depeng Dang

“…In this paper, we propose CropDP-KG, a knowledge graph for crop diseases and pests in China, which leverages natural language processing techniques to analyze data from the Chinese crop diseases and pests image-text database. …”
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Multi-Modal Social Media Analytics: A Sentiment Perception-Driven Framework in Nanjing Districts by Meng Xia, Zhengyang Lu, Feng Wang

“…By integrating computer vision techniques with natural language processing, we develop a comprehensive framework for analyzing public sentiment and attention patterns across eleven districts. …”
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Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach by Andre O. Francani, Marcos R. O. A. Maximo

“…Transformer-based architectures have dominated the state-of-the-art in natural language processing and computer vision tasks, such as image and video understanding. …”
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