How artificial intelligence can enable data classification for market sizing - Insights from applications in practice by L. Stallings, P. Bhat, J. Jacobs, K. Lynch, Q. Risch

“…We describe the evaluation and use of multiple natural language processing (NLP) and classification modeling methods. …”
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The Impact of Linguistic Variations on Emotion Detection: A Study of Regionally Specific Synthetic Datasets by Fernando Henrique Calderón Alvarado

“…Emotion detection is essential for natural language processing (NLP) applications such as social media analysis, customer service, and mental health monitoring. …”
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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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Artificial intelligence in educational technology and transformative approaches to English language using fuzzy framework with CRITIC-TOPSIS method by Jingdan Liu, Xujie Bao, Liji Chen

“…Machine learning algorithms analyze student performance to tailor content delivery, while natural language processing facilitates interactive learning through voice assistants and essay evaluation. …”
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The impact of conversational AI on consumer decision-making: A systematic review and cluster analysis by David Lopez-Lopez, Marc Bara Iniesta

“…This study presents a comprehensive analysis of the influence of conversational artificial intelligence (AI)—a subset of AI that enables machines to simulate human-like conversations through natural language processing (NLP)—on consumer decision-making within the digital marketing landscape. …”
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A survey on textual emotion cause extraction in social networks by Sancheng Peng, Lihong Cao, Guojun Wang, Zhouhao Ouyang, Yongmei Zhou, Shui Yu

“…Textual Emotion Cause Extraction (TECE) aims to automatically extract causes for a certain emotion in texts, which is an important research issue in natural language processing. It is different from the previous tasks of emotion recognition and emotion classification. …”
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Research on Medical Image Segmentation Based on SAM and Its Future Prospects by Kangxu Fan, Liang Liang, Hao Li, Weijun Situ, Wei Zhao, Ge Li

“…The rapid advancement of prompt-based models in natural language processing and image generation has revolutionized the field of image segmentation. …”
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ARTIFICIAL INTELLIGENCE AND ITS ECONOMIC CHALLENGES IN EU COUNTRIES: IMPLICATION FOR THE REPUBLIC OF MOLDOVA by MAIA PISANIUC

“…Global projections also estimate that, by 2030, around 70% of businesses will use at least one artificial intelligence technology, such as computer vision, natural language processing, virtual assistants, automation/robotics and machine learning. …”
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ElHBiAt: Electra Pre-Training Network Hybrid of BiLSTM, the Attention Layer to Aspect-Based Sentiment Analysis by Amin Ghanee Nezhad, Kia Jahanbin, Abolfazl Razzaghi Yamchi

“…Aspect-based sentiment analysis is one of the famous and practical subjects in natural language processing. Traditional sentiment analysis assigns a polarity to the whole text or document and does not consider the aspects of the text, so it cannot correctly show the author’s opinion. …”
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A Study on Systematic Improvement of Transformer Models for Object Pose Estimation by Jungwoo Lee, Jinho Suh

“…Transformer architecture, initially developed for natural language processing and time series analysis, has been successfully adapted to various generative models in several domains. …”
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Exploring Text Similarity in Human and AI-Generated Scientific Abstracts: A Comprehensive Analysis by Tugba Celikten, Aytug Onan

“…As data volumes increase, understanding the similarities and relationships between texts becomes essential, particularly in natural language processing (NLP) tasks such as text generation, summarization, and classification. …”
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Towards the Application of GraphRAG to Network Security by Marco Carvalho, Fitzroy Nembhard, Dhanish Mehta

“… The adoption of large language models (LLMs) has facilitated significant advancements in natural language processing. In a short space of time, LLMs have permeated a wide array of disciplines including healthcare, finance, education, etc. …”
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Assessment of depression and anxiety in young and old with a question-based computational language approach by Sverker Sikström, Bleona Kelmendi, Ninni Persson

“…We then analyse their responses with computational methods based on natural language processing (NLP). The results demonstrate that: (1) middle aged adults describe their mental health differently compared to younger adults; (2) where, for example, middle aged adults emphasise depression and loneliness whereas young adults list anxiety and financial concerns; (3) different semantic models are warranted for younger and middle aged adults; (4) compared to young participants, the middle aged participants described their mental health more accurately with words; (5) middle-aged adults have better mental health than younger adults as measured by semantic measures. …”
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ABERT: Adapting BERT model for efficient detection of human and AI-generated fake news by Jawaher Alghamdi, Yuqing Lin, Suhuai Luo

“…Transfer learning, particularly with pre-trained language models (PLMs) like BERT, has demonstrated exceptional performance in natural language processing (NLP) tasks. However, the computational expense of fine-tuning the entire model for domain-specific tasks remains a limitation. …”
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Neural correlates of thematic role assignment for passives in Standard Indonesian. by Bernard A J Jap, Yu-Yin Hsu, Stephen Politzer-Ahles

“…There is evidence that frequency at the sentence level plays a significant role in language processing. To address this potential confounding variable, we will test a language where the non-canonical sentences are more frequent and are comparable to the canonical sentences, namely Standard Indonesian. …”
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Towards solving NLP tasks with optimal transport loss by Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria

“…Most common loss functions in natural language processing (NLP), such as Kullback–Leibler (KL) and Jensen–Shannon (JS) divergences, do not base their computations on the properties of label coordinates. …”
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Survey on Construction Method of Temporal Knowledge Graph by LU Jiamin, ZHANG Jing, FENG Jun, AN Qi

“…As a bridge connecting data, knowledge, and intelligence, knowledge graph has been widely applied in fields such as search assistance, intelligent recommendation, question-answering systems, and natural language processing. However, with the expansion of application scenarios, static knowledge graph has shown limitations in handling dynamic knowledge. …”
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Comparative Study of SVM, KNN, and Naïve Bayes for Sentiment Analysis of Religious Application Reviews by Heti Aprilianti, Khothibul Umam, Maya Rini Handayani

“…These findings indicate that SVM is the most effective algorithm in this context, providing practical contributions for developers of the NU Online digital religious platform and contributing to research in Indonesian natural language processing.…”
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Automatic description of rock thin sections: A web application by Stalyn Paucar, Christian Mejia-Escobar, Victor Collaguazo

“…This study proposes an artificial intelligence-based approach that combines computer vision and natural language processing to generate both textual and verbal descriptions from images of rock thin sections. …”
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Entity and relation collaborative extraction approach based on multi-head attention and gated mechanism by Wei Zhao, Shan Zhao, Shuhui Chen, Tien-Hsiung Weng, WenJie Kang

“…Entity and relation extraction has been widely studied in natural language processing, and some joint methods have been proposed in recent years. …”
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