Special Issue on Recent Applications of Machine Learning in Natural Language Processing (NLP)

1. Introduction

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. The rise of large language models (LLMs) has further transformed tasks such as question answering and multilingual communication. With the integration of multimodal data—text, speech, and vision—NLP systems continue to expand in scope and sophistication.

This Special Issue, entitled “Recent Applications of Machine Learning in Natural Language Processing (NLP)”, presents eight peer-reviewed papers showcasing recent research progress at the intersection of machine learning and NLP. These contributions span key areas such as named entity recognition, multimodal sentiment analysis, domain-specific language modeling, and knowledge-enhanced methods.

2. Overview of Contributions

This Special Issue comprises eight papers, each outlining novel approaches and findings in applying machine learning to NLP tasks. Below is an overview of each paper: Xiao et al. [1] proposed a novel few-shot named entity recognition method by integrating large language models with metric learning, improving performance in low-resource settings. Jiang et al. [2] developed a dynamic topic modeling framework for tracking 6G technology trends using patent texts and LLM-enhanced summarization. Tian et al. [3] introduced a collaborative learning approach for video temporal grounding, achieving high accuracy by leveraging vision–language model interactions. Doumanas et al. [4] conducted a comparative study of GPT-4 and Mistral in ontology engineering tasks, demonstrating the strengths and trade-offs of each model. Cao et al. [5] presented a prompt-based NER system for shearer maintenance text, extracting entities in a specialized industrial domain without fine-tuning. Yang et al. [6] proposed a multi-task pre-training framework to enhance few-shot multimodal sentiment analysis by aligning textual and visual features. Iaroshev et al. [7] evaluated retrieval-augmented generation models for financial report question answering, showing the importance of retrieval quality and structure. Gao et al. [8] introduced a graph convolutional network enhanced with sentiment support for aspect-level sentiment classification, achieving improved accuracy.

3. Conclusions

In summary, the contributions in this Special Issue collectively illustrate the breadth and depth of current research on machine learning applications in NLP. The eight papers cover a range of essential problems and methodologies: from advancing few-shot learning techniques and prompt-based models for specialized information extraction, to leveraging multi-modal and collaborative learning for integrating vision and language, and enhancing language model capabilities for knowledge-rich tasks like ontology management and financial document Q&A. A unifying theme is the continual push to overcome data scarcity and complexity by cleverly utilizing large pre-trained models, innovative training strategies, or incorporating domain-specific knowledge. These students not only advance their respective fields but also underscore the versatility of machine learning in tackling diverse NLP challenges across domains (from social media and multimedia content to industrial and financial text analytics). We believe that the insights and results presented in this Special Issue will inspire further research and development in the NLP community. Open challenges remain, such as improving model interpretability, handling low-resource languages, and ensuring the ethical use of AI in language tasks, which future studies can build upon using the foundations laid by these papers.