A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities

Chang Weng, Scott Rood, Mehdi Ali Ramezani, Amir Aslani, Reza Zarrab +3 more
12/20/2024
cs.CL

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Abstract

Natural Language Processing (NLP) is widely used in fields like machine translation and sentiment analysis. However, traditional NLP models struggle with accuracy and efficiency. This paper introduces Deep Convolutional Neural Networks (DCNN) into NLP to address these issues. By integrating DCNN, machine learning (ML) algorithms, and generative adversarial networks (GAN), the study improves language understanding, reduces ambiguity, and enhances task performance. The high-performance NLP model shows a 10% improvement in segmentation accuracy and a 4% increase in recall rate compared to traditional models. This integrated approach excels in tasks such as word segmentation, part-of-speech tagging, machine translation, and text classification, offering better recognition accuracy and processing efficiency.

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Cite this paper

@article{weng2024thorough,
  title  = {A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities},
  author = {Chang Weng and Scott Rood and Mehdi Ali Ramezani and Amir Aslani and Reza Zarrab and Wang Zwuo and Sanjeev Salimans and Tim Satheesh},
  year   = {2024},
  eprint = {2412.15900},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.15900v1}
}

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