Deep learning for natural language processing (NLP) is a prominent research area focused on leveraging neural network architectures to understand, generate, and manipulate human language. Research papers in this domain explore models such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), gated recurrent units (GRUs), convolutional neural networks (CNNs), attention mechanisms, and transformer-based architectures (e.g., BERT, GPT, RoBERTa, T5) for tasks including sentiment analysis, machine translation, text summarization, question answering, named entity recognition, and conversational AI. Key contributions include contextualized word representations, pretraining and fine-tuning strategies, handling sequential and long-range dependencies, and integrating multimodal information. Recent studies also address challenges such as low-resource language modeling, domain adaptation, explainability, and computational efficiency for deployment on edge and cloud platforms. By applying deep learning, NLP research aims to create intelligent, adaptive, and scalable language understanding systems capable of supporting diverse applications in healthcare, finance, social media, and human-computer interaction.