ADVANCES IN NATURAL LANGUAGE PROCESSING: A SURVEY OF TECHNIQUES - Alladi Deekshith, (International Journal of Innovations in Engineering Research and Technology, 2024)
Natural Language Processing (NLP) has witnessed remarkable advancements over the past few decades, transforming the way machines understand and interact with human language. This survey provides a comprehensive overview of the key techniques and methodologies that have propelled the field forward, highlighting both traditional approaches and contemporary innovations. We begin by discussing foundational NLP techniques such as tokenization, part-of-speech tagging, and syntactic parsing, which laid the groundwork for understanding language structure. The evolution of statistical methods, including Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs), is explored as a significant advancement in the probabilistic modeling of language. The survey then delves into the rise of machine learning approaches, particularly supervised and unsupervised learning, which have revolutionized various NLP tasks such as sentiment analysis, named entity recognition, and machine translation. We examine the impact of deep learning, focusing on architectures like Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Convolutional Neural Networks (CNNs) that have enabled significant improvements in performance across a range of applications. The introduction of transformer models, particularly the attention mechanism and BERT (Bidirectional Encoder Representations from Transformers), marks a paradigm shift in how contextual information is captured, leading to state-of-the-art results in numerous NLP benchmarks. In addition to technical advancements, the survey addresses the challenges that persist in NLP, including issues of bias in language models, the necessity for large annotated datasets, and the importance of explainability in AI systems. We discuss ongoing research efforts aimed at mitigating these challenges, including techniques for domain adaptation, few-shot learning, and unsupervised representation learning. This survey aims to provide researchers and practitioners with a clear understanding of the trajectory of NLP techniques, illustrating how traditional methods have evolved into sophisticated deep learning models. We conclude by highlighting future directions for research in NLP, emphasizing the need for interdisciplinary approaches that integrate linguistics, cognitive science, and ethical considerations to build more robust, fair, and interpretable NLP systems. Through this comprehensive survey, we seek to inspire further exploration and innovation in the field of Natural Language Processing, paving the way for applications that can better understand and generate human language in diverse contexts.