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

• URL: http://arxiv.org/abs/2412.15900v1
• Date: Fri, 20 Dec 2024 13:53:41 GMT
• Title: A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities
• Authors: Chang Weng, Scott Rood, Mehdi Ali Ramezani, Amir Aslani, Reza Zarrab, Wang Zwuo, Sanjeev Salimans, Tim Satheesh,
• Abstract summary: This paper introduces Deep Convolutional Neural Networks (DCNN) into Natural Language Processing. By integrating DCNN, machine learning 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.
• Score: 0.0
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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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