Author(s)

Zi'an He ¹

Affiliation(s)

¹ School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois, US

Corresponding Author

Zi'an He

ABSTRACT

Natural Language Processing (NLP), as a key branch of computer science and artificial intelligence, aims to enable machines to understand and generate human language. Although early rule-based methods and statistical learning models have made some progress in dealing with the complexity and diversity of language, there are limitations, such as relying on specific language grammar and vocabulary, and difficulty in handling ambiguity and complex contexts. However, NLP still faces challenges such as overfitting, underfitting, and model optimization. Based on this, this article analyzes how deep learning improves the accuracy and efficiency of NLP tasks by introducing multi-layer neural network architectures such as recurrent neural networks (RNN), long short-term memory networks (LSTM), and transformers. Especially in terms of model optimization techniques, strategies such as parameter adjustment, handling overfitting and underfitting, and specific applications of emerging optimization algorithms were explored. This article aims to provide researchers and developers with a deep understanding of NLP challenges and effective solutions, in order to promote the further development and application of NLP technology.

KEYWORDS

Natural Language Processing, Deep Learning, Model Optimization

CITE THIS PAPER

Zi'an He, Optimization and Application of Natural Language Processing Models Based on Deep Learning. Journal of Artificial Intelligence Practice (2024) Vol. 7: 109-115. DOI: http://dx.doi.org/10.23977/jaip.2024.070117.

REFERENCES

[1] An Junxiu, Jiang Sichang. A comprehensive review of word vector models for natural language processing [J]. Computer Technology and Development, 2023, 33(12): 17-22.
[2] Ge Huibin, Wang Dexin, Zheng Tao, et al. Research on the transfer of natural language processing models to domestic deep learning platforms [J]. Computer Science, 2024, 51(01): 50-59.
[3] Dai Xiaohong. Research on text classification algorithms for natural language based on deep learning [D]. Hebei University of Engineering, 2023.
[4] Lu Xin. Research on feature space backdoor attack methods for natural language processing models [D]. Nanchang University, 2023.
[5] Yang Ruisen. Research on Chinese named entity recognition models based on deep learning [D]. Zhengzhou University of Light Industry, 2023.

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