Natural Language Processing Advancements By Deep Learning: A Survey

By Amirsina Torfi, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavvaf, Edward A. Fox. 2020

266 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Sarath Chandar
2. Gabriele Prato
3. Irwin King
4. Zenglin Xu
5. Quan Z. Sheng
6. Shen Furao
7. Hu Wang
8. Xiangli Yang

In Computer Vision and Pattern Recognition

1. Sarath Chandar
2. Gabriele Prato
3. Irwin King
4. Zenglin Xu
5. Quan Z. Sheng
6. Shen Furao
7. Hu Wang
8. Xiangli Yang

In Natural Language Processing

1. Sarath Chandar
2. Gabriele Prato
3. Irwin King
4. Zenglin Xu
5. Quan Z. Sheng
6. Hu Wang
7. Xiangli Yang
8. Irina Rish

Citations in the corpus

• Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers (2021, background): “Over the past decade, deep learning has made tremendous progress in multiple fields, especially in vision (Alam et al., 2020) and natural language processing (Torfi et al., 2020).”
• A Survey on Deep Semi-Supervised Learning (2021, background): “D EEP learning has been an active research field with abundant applications in pattern recognition [1], [2], data mining [3], statistical learning [4], computer vision [5], [6], natural language processing [7], [8], etc.”
• Multi-document Summarization via Deep Learning Techniques: A Survey (2020, background): “Moreover, deep learning based models reduce dependence on manual feature extraction and pre-knowledge in the field of linguistics, drastically improving the ease of engineering [147].”
• Image Data Augmentation for Deep Learning: A Survey (2022, background): “Deep learning has made incredible progress in many fields, including computer vision(CV) [Hassaballah and Awad, 2020], recommender system (RS) [Liu et al. , 2021a], natu-ral language processing (NLP) [Torfi et al. , 2020] and so on.”