Advancements in Deep Learning for Natural Language Processing
Abstract
This research paper explores recent advancements in deep learning techniques applied to Natural Language Processing (NLP). Deep learning models, such as neural networks and transformers, have demonstrated remarkable performance in various NLP tasks, including language translation, sentiment analysis, and text generation. This paper reviews key innovations in deep learning for NLP, discusses the challenges and opportunities, and presents case studies of successful applications. Additionally, it addresses the ethical implications and considerations surrounding NLP models. The insights from this study contribute to the ongoing development of NLP technologies and their ethical application in real-world scenarios.
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References
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