Jul 2026· International Journal of Advanced Research in Science, Communication and Technology· pp. 539· 0 citations· 8 references
TL;DR
This paper provides a comprehensive survey of NLP, discussing its components, historical evolution, applications, datasets, evaluation metrics, recent advancements, and challenges, and explores future research directions to address low-resource languages, model fairness, and real-world applicability.
Abstract
Natural Language Processing (NLP) has become a cornerstone of artificial intelligence, enabling machines to process, understand, and generate human language. With the increasing adoption of machine learning, deep learning, and large-scale transformer models, NLP has made significant progress in the past decade. Modern NLP systems are used in applications ranging from machine translation, sentiment analysis, chatbots, and automated summarization to knowledge extraction and conversational agents. Transformer-based architectures and large language models (LLMs) have drastically improved context understanding, semantic representation, and generation quality. This paper provides a comprehensive survey of NLP, discussing its components, historical evolution, applications, datasets, evaluation metrics, recent advancements, and challenges. A detailed literature review based on studies from 2022–2026 is presented, highlighting the role of transformer models, deep learning architectures, and emerging trends such as multi-lingual NLP, domain-specific models, and ethical considerations. Finally, the paper explores future research directions to address low-resource languages, model fairness, and real-world applicability.
A comprehensive review of the evolution of NLP from traditional rule-based approaches to modern transformer models including BERT and GPT demonstrates that NLP continues to transform intelligent systems and is expected to play an increasingly significant role in the development of next-generation AI technologies.
P. Kalaiselvi· International Journal of Eme...· 0 citations
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.
Dr. Pradeep Kumar Atulker, Dr. Rahul Kumar Hindustani, Ravi Shankar Nanduri et al.· Genetics and Molecular Resea...· 0 citations
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.
P. Peykani, V. Charles, Ali Emrouznejad et al.· Archives of Computational Me...· 0 citations
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.
Jun Wu· Applied and Computational En...· 0 citations
The rapid evolution of natural language processing has driven sentiment analysis from simple lexicon based tools to sophisticated generative large language models. This systematic review charts that architectural transition, covering three distinct eras: rule based and statistical machine learning, hybrid deep learning architectures (notably CNN LSTM), and the current paradigm of transformer based and generative LLMs. Following PRISMA and Kitchenham guidelines, we synthesise findings from high impact publications (2020–2025) across IEEE, Elsevier, Springer, and ACL. The performance measures, contextual reasoning abilities, and other challenges like model variability, sarcasm detection, multimodal fusion, and interpretability form part of our analysis. Additionally, issues surrounding sustainability are addressed via Green AI techniques such as quantization and knowledge distillation. The findings show that although discriminative fine-tuned models continue to perform excellently in narrow classification tasks, generative LLMs possess impressive zero shot reasoning and flexibility but have issues with inconsistency and lack of transparency. We conclude by identifying key research gaps – deterministic benchmarking, uncertainty aware calibration, autonomous multimodal reasoning, and agentic explainability – and propose a roadmap for future work. This survey serves as a comprehensive resource for researchers and practitioners navigating the shifting landscape of sentiment analysis.
Khushi Gautam and Rupali Bhartiya· International Journal of Adv...· 0 citations
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.