Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application

Keyu Chen, Cheng Fei, Ziqian Bi, Junyu Liu, Benji Peng +21 more
10/30/2024
cs.CLcs.HC

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Abstract

With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence. As artificial intelligence continues to revolutionize fields from healthcare to finance, NLP techniques such as tokenization, text classification, and entity recognition are essential for processing and understanding human language. This paper discusses advanced data preprocessing techniques and the use of frameworks like Hugging Face for implementing transformer-based models. Additionally, it highlights challenges such as handling multilingual data, reducing bias, and ensuring model robustness. By addressing key aspects of data processing and model fine-tuning, this work aims to provide insights into deploying effective and ethically sound AI solutions.

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Cite this paper

@article{chen2024deep,
  title  = {Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application},
  author = {Keyu Chen and Cheng Fei and Ziqian Bi and Junyu Liu and Benji Peng and Sen Zhang and Xuanhe Pan and Jiawei Xu and Jinlang Wang and Caitlyn Heqi Yin and Yichao Zhang and Pohsun Feng and Yizhu Wen and Tianyang Wang and Ming Li and Jintao Ren and Qian Niu and Silin Chen and Weiche Hsieh and Lawrence K. Q. Yan and Chia Xin Liang and Han Xu and Hong-Ming Tseng and Xinyuan Song and Zekun Jiang and Ming Liu},
  year   = {2024},
  eprint = {2411.05026},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.05026v3}
}

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