# Deep Learning applied to NLP

arXiv: https://arxiv.org/abs/1703.03091 · HTML: https://mlsanity.com/paper/1703.03091/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2017.

## Built on by (most cited first, 11 of 11)

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- [Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes](https://mlsanity.com/paper/2412.13573/) (2024)
- [TI-Capsule: Capsule Network for Stock Exchange Prediction](https://mlsanity.com/paper/2102.07718/) (2021)
- [Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View Data](https://mlsanity.com/paper/2403.10663/) (2024)
- [Computational Approaches to Arabic-English Code-Switching](https://mlsanity.com/paper/2410.13318/) (2024)
- [ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning](https://mlsanity.com/paper/2202.02928/) (2022)
- [Deep neural networks for fine-grained surveillance of overdose mortality](https://mlsanity.com/paper/2202.12448/) (2022)
- [On the Identification of the Energy related Issues from the App Reviews](https://mlsanity.com/paper/2304.11292/) (2023)
- [Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset](https://mlsanity.com/paper/2308.13545/) (2023)
- [Cross-Modality Clustering-based Self-Labeling for Multimodal Data Classification](https://mlsanity.com/paper/2408.02568/) (2024)

## Abstract (arXiv)

Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP.