This is our latest selection of worldwide publications and patents in english on Natural Language Processing (NLP), between many scientific online journals, classified and focused on natural language processing, tokenization, stemming, lemmatization, part-of-speech, named entity recognition and sentiment analysis.
Graph-to-Text Generation with Bidirectional Dual Cross-Attention and Concatenation
Published on 2025-03-11 by Elias Lemuye Jimale, Wenyu Chen, Mugahed A. Al-antari, Yeong Hyeon Gu, Victor Kwaku Agbesi, Wasif Feroze, Feidu Akmel, Juhar Mohammed Assefa, Ali Shahzad @MDPI
Abstract: Graph-to-text generation (G2T) involves converting structured graph data into natural language text, a task made challenging by the need for encoders to capture the entities and their relationships within the graph effectively. While transformer-based encoders have advanced natural language processing, their reliance on linearized data often obscures the complex interrelationships in graph structures, leading to structural loss. Conversely, graph attention networks excel at capturing graph struc[...]
Our summary: Proposal of a novel mechanism for integrating transformer-based and graph attention encoders to improve graph-to-text generation tasks, achieving higher BLEU and METEOR scores on benchmark datasets, showcasing potential for future research.
Graph-to-text generation, Bidirectional Dual Cross-Attention, Concatenation, Transformer-based encoders
Publication
Named Entity Recognition in Online Medical Consultation Using Deep Learning
Published on 2025-03-11 by Ze Hu, Wenjun Li, Hongyu Yang @MDPI
Abstract: Named entity recognition in online medical consultation aims to address the challenge of identifying various types of medical entities within complex and unstructured social text in the context of online medical consultations. This can provide important data support for constructing more powerful online medical consultation knowledge graphs and improving virtual intelligent health assistants. A dataset of 26 medical entity types for named entity recognition for online medical consultations is fi[...]
Our summary: Named entity recognition in online medical consultation addresses the challenge of identifying medical entities in unstructured text, constructing knowledge graphs, and improving virtual health assistants. The proposed deep learning approach outperforms the state-of-the-art method in identifying 26 medical entity types with an average F1 score of 85.47%, supporting real-time intelligent medical decision-making.
Named Entity Recognition, Online Medical Consultation, Deep Learning, Knowledge Graphs
Publication
The Rise of Reasoning Large Language Models for Consumer Complaint Detection and Classification
Published on 2025-03-07 by Konstantinos I. Roumeliotis, Nikolaos D. Tselikas, Dimitrios K. Nasiopoulos @MDPI
Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing (NLP) tasks, but their effectiveness in real-world consumer complaint classification without fine-tuning remains uncertain. Zero-shot classification offers a promising solution by enabling models to categorize consumer complaints without prior exposure to labeled training data, making it valuable for handling emerging issues and dynamic complaint categories in finance. However, this task[...]
Our summary: The study evaluates the performance of leading large language models and reasoning models for zero-shot classification of consumer complaints in finance, highlighting their strengths and limitations. Integration of reasoning models into classification workflows can enhance complaint resolution automation and improve customer service efficiency.
reasoning models, large language models, zero-shot classification, consumer complaints
Publication
Perspectives Through an Academic Lens
Published on 2025-03-06 by Iulian �ntorsureanu, Simona-Vasilica Oprea, Adela B�ra, Drago? Vespan @MDPI
Abstract: In this paper, we investigated the role of generative AI in education in academic publications extracted from Web of Science (3506 records; 2019–2024). The proposed methodology included three main streams: (1) Monthly analysis trends; top-ranking research areas, keywords and universities; frequency of keywords over time; a keyword co-occurrence map; collaboration networks; and a Sankey diagram illustrating the relationship between AI-related terms, publication years and research ar[...]
Our summary: Role of generative AI in education analyzed through academic publications from Web of Science, including trends, sentiment analysis, and topic modeling. AI applications in specialized fields and emerging topics identified.
generative AI, education, academic publications, Web of Science
Publication
A Novel Approach to Integrating Community Knowledge into Fuzzy Logic-Adapted Spatial Modeling in the Analysis of Natural Resource Conflicts
Published on 2025-03-06 by Lawrence Ibeh, Kyriakos Kouveliotis, Deepak Rajendra Unune, Nguyen Manh Cuong, Noah Mutai, Anastasios Fountis, Svitlana Samoylenko, Priyadarshini Pattanaik, Sushma Kumari, Benjamin Bensam Sambiri, Sulekha Mohamud, Alina Baskakova @MDPI
Abstract: Resource conflicts constitute a major global issue in areas rich in natural resources. The modeling of factors influencing natural resource conflicts (NRCs), including environmental, health, socio-economic, political, and legal aspects, presents a significant challenge compounded by inadequate data. Quantitative research frequently emphasizes large-scale conflicts. This study presents a novel multilevel approach, SEFLAME-CM—Spatially Explicit Fuzzy Logic-Adapted Model for Conflict [...]
Our summary: A novel approach integrating community knowledge into fuzzy logic-adapted spatial modeling for analyzing natural resource conflicts. SEFLAME-CM model shows robust correlation between conflict risk and drivers at community level. Environmental variables play key role in conflicts, while socio-economic and political factors are of lesser importance.
fuzzy logic, spatial modeling, natural resource conflicts, community knowledge
Publication
A Contextual and Transformer-Based Approach for Improved Detection
Published on 2025-03-06 by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro @MDPI
Abstract: Sarcasm detection is a crucial task in natural language processing (NLP), particularly in sentiment analysis and opinion mining, where sarcasm can distort sentiment interpretation. Accurately identifying sarcasm remains challenging due to its context-dependent nature and linguistic complexity across informal text sources like social media and conversational dialogues. This study utilizes three benchmark datasets, namely, News Headlines, Mustard, and Reddit (SARC), which contain diverse sarcastic[...]
Our summary: Utilizing transformer-based models and contextual summarization to enhance sarcasm detection accuracy and efficiency. Leveraging benchmark datasets to address the challenges of identifying sarcasm in informal text sources. Performance evaluation based on accuracy, F1 score, and Jaccard coefficient showcasing the effectiveness of the proposed methodology.
Transformer-Based, Sarcasm Detection, Contextual Approach, Improved Detection
Publication
Voice-based conversation artificial intelligence for point-of-sale systems
Patent published on the 2025-03-06 in WO under Ref WO2025049246 by PREDICTSPRING INC [US] (Mangtani Nitin [us], Garimella Sandilya [us], Chadalawada Viswanth [us], Sahu Madhav [us])
Abstract: A conversational artificial intelligence (Al) point-of-sale (POS) system uses generative Al to help a store associate complete tasks in a retail environment. This POS system combines four technologies: speech recognition; natural language processing; acting in response to verbal command in the context of a retail store and a POS device; and providing bi-directional and fully conversational responses. The Al, which runs in an app on a smartphone, tablet, or other mobile device, uses a machine lea[...]
Our summary: Conversational AI POS system uses generative AI to assist store associates in retail tasks, combining speech recognition, natural language processing, and bi-directional responses. The AI, running on mobile devices, utilizes ML models trained on support tickets and product documentation to understand dependencies and provide appropriate responses.
voice-based conversation, artificial intelligence, point-of-sale systems, generative AI
Patent
Image interpretation model development
Patent published on the 2025-03-06 in WO under Ref WO2025048865 by SYNTHESIS HEALTH INC [CA] (Reicher Murray [ca], Kaura Deepak [ca])
Abstract: An image classification model, e.g., a neural network model, may be trained on a set of training medical imaging exams each including a training report and a training medical image. A model generation module or device may, for each of the training medical imaging exams: use finding item criteria to reorganize text of the training report into a list of finding items, each associated with text extracted from the training report text, use natural language processing to analyze the resultant text as[...]
Our summary: Training a neural network model on medical imaging exams with associated finding items and classifications extracted from training reports.
image interpretation, model development, neural network, medical imaging
Patent
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