Applications of Natural Language Processing: A Comprehensive Study

NATURAL LANGUAGE PROCESSING USING DEEP LEARNING: A COMPREHENSIVE REVIEW OF MODELS, TECHNIQUES, AND FUTURE DIRECTIONS

Natural language processing (NLP) has emerged as a key focus of AI research for the analysis, interpretation, extraction, summarisation, and generation of human language. The vast amount of unstructured textual data in scientific research, electronic health records, clinical notes, radiology reports, public health documents, and digital health platforms has driven the demand for sophisticated computational tools and techniques capable of extracting structured and actionable knowledge from language. NLP has been greatly advanced by deep learning, which allows for automatic representation learning, understanding context, modeling sequences, and generating large amounts of language by means of structures like CNN, RNN, LSTM, GRU, attention mechanisms, transformers, and large language models. This review aims to present a detailed overview of deep learning-based NLP models, methods, applications, challenges, and future directions, focusing on biomedical informatics, clinical text mining, digital health and biomathematical relevance. It has numerous applications such as biomedical literature mining, named entity recognition, relation extraction, clinical decision support, pharmacovigilance, radiology report generation, public health surveillance, and construction of knowledge graph. The specific focus lies in the application of NLP to identify biological entities, clinical variables and quantitative evidence that can be used to support biomathematical modeling. There are several current challenges such as domain shift, privacy, hallucination, bias, interpretability, and reproducibility. The success of future progress relies on reliable, comprehensible, domain specific and clinically verified NLP systems.

The Versatility of Large Language Models: A Comprehensive Review and Structured Survey of Architectures, Applications, Challenges, and Future Trajectories

This survey reviews the evolution of language models from early statistical approaches to modern Transformer-based architectures and summarizes key developments, including attention mechanisms, scaling laws, alignment techniques, and efficient inference methods.

Introduction to Natural Language Processing in High-Stakes Domains

AI-Powered Language Tools and Their Influence on Modern Linguistics

The high rate of development of artificially intelligence (AI) has significantly transformed the linguistic profession by introducing the use of AI-based language applications. Machine learning, deep learning, and natural language processing (NLP) are the driving forces of these tools redefining the methods of how language is studied, generated, and maintained. Since automated translation and speech recognition systems, AI systems are now at the center of linguistic studies and applications of language in practice. In the given article, we derive detailed research on how AI-based language tools impact the contemporary linguistics. It explores theoretical and historical developments, methodological and practical changes within the subdomains of linguistics. A systematic review of the literature brings out main milestones, trends in the research, and shortcomings of the available methods. The suggested methodology is going to assess AI-based linguistic tools based on both qualitative and quantitative scales, such as accuracy, linguistic validity, scalability, and interpretability. The findings indicate that AI-enabled applications can significantly improve the efficiency of the analytical process and reveal the linguistic patterns that are not available to ordinary analysis. Yet, another problem like bias, explainability and ethics issues is significant. The research provides a conclusion that although AI has become an inseparable part of linguistics today, there is a need to establish a balanced approach of using computational methods and knowledge of human linguists to be sustainable and ethical.

A Review of Long-Context Processing Ability in Large Language Models: Technical Progress and Challenges

In recent years, large language models (LLMs) have achieved significant results in natural language processing. They are applied to various tasks, including text generation, question answering, automatic summarization, code generation, and complex reasoning. With the increasingly complex real scenarios, the length of input text that models need to deal with also grows. Thus, the long-context processing ability of language models has gradually become an important factor in evaluating the practicability of LLMs. This paper gives an introduction to the long-context processing ability of large language models. It first introduces the background of large language models and the basic concept of long-context processing. It then summarizes the main technical methods of long-context modeling, such as improving positional encoding, training stage expansion, inference-stage optimization, and architecture-level innovation. Third, the paper also discusses the use of long-context ability in long-document question answering, long-text summarization, multi-document integration, code understanding and long-context evaluation tasks. Then, summarize the current main challenges and prospects of research work. This paper argues that the ability of long context should not only come from increasing the context window, but also from the ability of the model to locate, integrate and reason about important information in long text.

Large Language Model Technologies: Progress, Problems and Prospects

Large language models (LLMs) are built on the classic Transformer architecture and have become a core driving force for the rapid development of modern artificial intelligence. This paper presents a systematic review of LLMs, elaborating on their fundamental working principles, mainstream open-source models, effective lightweight optimization methods, retrieval-augmented generation frameworks and key human-value-aligned technologies. Nowadays, LLMs have been widely applied in practice. Typical scenarios include intelligent text generation, professional knowledge-based question answering and automated code generation, delivering remarkable value to both industries and academia. However, their large-scale industrial application is still restricted by multiple challenges. The major issues involve content hallucination, poor model interpretability, excessive computing resource consumption, potential ethical risks and unsatisfactory multimodal integration capability. This paper also forecasts the future development directions of LLMs, such as lightweight deployment on edge devices, safety-focused human value alignment, in-depth cross-modal fusion and customized large models for vertical industries. Additionally, it collects a number of representative cases, which can offer solid references and practical guidance for relevant researchers and engineering practitioners to carry out further studies.