1. Introduction
Deep learning has revolutionized natural language processing...
2. Background
Traditional NLP approaches versus deep learning methods...
3. Transformer Architecture
The game-changing architecture for NLP tasks...
4. Applications
Real-world applications and case studies...
5. Future Directions
Emerging trends and future research directions...
References
[1] Vaswani, A. et al. (2017). Attention Is All You Need.
[2] Devlin, J. et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers.
Deep Learning Approaches for Natural Language Processing: A Survey
Emma Brown
Oxford University
DOI: 10.1234/journal.v1.1.003
Published: January 15, 2024
Review Articles
Abstract
This survey paper reviews recent advances in deep learning for natural language processing (NLP). We cover transformer architectures, BERT models, and GPT variants, analyzing their performance on various NLP tasks including sentiment analysis, machine translation, and question answering. The review includes a comparative analysis of model architectures and training methodologies.
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Article Info
-
Issue:
Vol. 1 No. 1 (2024): Inaugural Issue -
Section:
Review Articles -
Submitted:
December 01, 2023 -
Published:
January 15, 2024 -
DOI:
10.1234/journal.v1.1.003
How to Cite
Emma Brown (2024). "Deep Learning Approaches for Natural Language Processing: A Survey." Abhidakara Jurnal, 1(1). doi:10.1234/journal.v1.1.003