Evolution and advancements in natural language processing: from representation learning to deep neural networks

Haoyu Bian

doi:10.1117/12.3050653

27 September 2024 Evolution and advancements in natural language processing: from representation learning to deep neural networks

Haoyu Bian

Author Affiliations +

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132810Q (2024) https://doi.org/10.1117/12.3050653
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

Natural Language Processing (NLP) has undergone a remarkable transformation, with representation learning playing a pivotal role in reshaping the field. This review explores the evolution of NLP representation learning, from traditional feature engineering to modern techniques, focusing on distributed representations and deep learning methods. We delve into the significance of word embeddings like Word2Vec and GloVe, which have become instrumental in enhancing text processing. Furthermore, we examine common deep learning architectures, including Multilayer Perceptrons (MLP) and Convolutional Neural Networks (CNNs), highlighting their role in feature extraction from textual data. Additionally, optimization methods crucial for efficient deep neural network training are discussed. This review provides a comprehensive overview of the advancements in NLP architecture, emphasizing the enduring impact of prior research and the promise of future innovations.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haoyu Bian "Evolution and advancements in natural language processing: from representation learning to deep neural networks", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132810Q (27 September 2024); https://doi.org/10.1117/12.3050653

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