Special Issue on Recent Applications of Machine Learning in Natural Language Processing (NLP)
Source abstract
Abstract
Machine learning has profoundly advanced natural language processing (NLP), enabling more intelligent and human-like interaction across various applications, such as sentiment analysis, entity recognition, syntax parsing, and machine translation [...]
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Text and Document Classification Technologies · Educational and Technological Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
Faculty of Engineering, Gifu University, Gifu 501-1193, Japan
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
References (8)
Show source-provided references
1. Xiao, Y., Zou, J., and Yang, Q. (2025). Advancing Few-Shot Named Entity Recognition with Large Language Model. Appl. Sci., 15.10.3390/app15073838 ↗
2. Jiang, J., Ying, F., and Dhuny, R. (2025). Unveiling Technological Evolution with a Patent-Based Dynamic Topic Modeling Framework: A Case Study of Advanced 6G Technologies. Appl. Sci., 15.10.3390/app15073783 ↗
3. Tian, Y., Guo, X., Wang, J., Li, B., and Zhou, S. (2025). Video Temporal Grounding with Multi-Model Collaborative Learning. Appl. Sci., 15.10.3390/app15063072 ↗
4. Doumanas, D., Soularidis, A., Spiliotopoulos, D., Vassilakis, C., and Kotis, K. (2025). Fine-Tuning Large Language Models for Ontology Engineering: A Comparative Analysis of GPT-4 and Mistral. Appl. Sci., 15.10.3390/app15042146 ↗
5. Cao, X., Shi, L., Wang, X., Duan, Y., Yang, X., and Zhang, X. (2025). MCP: A Named Entity Recognition Method for Shearer Maintenance Based on Multi-Level Clue-Guided Prompt Learning. Appl. Sci., 15.10.3390/app15042106 ↗
6. Yang, J., Cao, J., and Duan, C. (2025). Multi-Task Supervised Alignment Pre-Training for Few-Shot Multimodal Sentiment Analysis. Appl. Sci., 15.10.3390/app15042095 ↗
7. Iaroshev, I., Pillai, R., Vaglietti, L., and Hanne, T. (2024). Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering. Appl. Sci., 14.10.3390/app14209318 ↗
8. Gao, R., Jiang, L., Zou, Z., Li, Y., and Hu, Y. (2024). A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis. Appl. Sci., 14.10.3390/app14072738 ↗