Natural Language Processing Advancements By Deep Learning: A Survey

TL;DR

<3-5 sentence high-level summary>The surveyed work addresses how deep learning has transformed Natural Language Processing by replacing hand-crafted features with data-driven representations and end-to-end architectures. It surveys fundamental concepts (feature representations, seq2seq, reinforcement learning), benchmark datasets, and DL-enabled NLP tasks spanning POS tagging, parsing, SRL, NER, MT, QA, summarization, and dialogue systems, highlighting architectural advances such as attention and encoder–decoder frameworks. A central theme is the shift from traditional rule-based and statistical methods to neural models (e.g., RNNs, CNNs, transformers) that learn representations and task-specific mappings, with formal learning objectives like $L_{CE} = -\sum_{t=1}^{L} \log p_{\theta}(y_t|y_{t-1},s_t,X)$. The work emphasizes evaluation challenges and practical implications for deploying DL-based NLP systems, while outlining open problems and directions for future research and datasets.

Abstract

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.

Figures (18)

• Figure 1: The general architecture of a MLP.
• Figure 2: A typical CNN architecture for object detection. The network provides a feature representation with attention to the specific region of an image (example shown on the left) that contains the object of interest. Out of the multiple regions represented (see an ordering of the image blocks, giving image pixel intensity, on the right) by the network, the one with the highest score will be selected as the main candidate.
• Figure 3: Recurrent Neural Network (RNN), summarized on the left, expanded on the right, for $N$ timesteps, with $X$ indicating input, $h$ hidden layer, and $O$ output
• Figure 4: Schematic of an Autoencoder
• Figure 5: Generative Adversarial Networks