Next-Generation Natural Language Processing: Technologies, Applications, and Challenges
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.