An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools

By Ivano Lauriola, Alberto Lavelli, Fabio Aiolli. Neurocomputing, 2021

579 citations recorded by the source corpus.

Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that involves the design and implementation of systems and algorithms able to interact through human language. Thanks to the recent advances of deep learning, NLP applications have received an unprecedented boost in performance. In this paper, we present a survey of the application of deep learning techniques in NLP, with a focus on the various tasks where deep learning is demonstrating stronger impact. Additionally, we explore, describe, and revise the main resources in NLP research, including software, hardware, and popular corpora. Finally, we emphasize the main limits of deep learning in NLP and current research directions.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/106176d796eda5efcd8a4d84ce98267350d679b9

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. Fan Yang
2. Ninghao Liu
3. Mengnan Du
4. Huiqi Deng
5. Shuaiqiang Wang
6. Haiyan Zhao
7. Hengyi Cai
8. Dawei Yin

Citations in the corpus

• Explainability for Large Language Models: A Survey (2023, methodology): “From the application view, traditional ML models focus on low-level pattern recognition tasks such as parsing and morphological analysis, whereas LLMs can handle high-level reasoning tasks such as question answering and commonsense reasoning (Lauriola et al., 2022).”