Optimization and Application of Natural Language Processing Models Based on Deep Learning
DOI: 10.23977/jaip.2024.070117 | Downloads: 7 | Views: 138
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 HeABSTRACT
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 OptimizationCITE 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
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