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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2412.15900 |
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| _version_ | 1866916535968202752 |
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| author | Weng, Chang Rood, Scott Ramezani, Mehdi Ali Aslani, Amir Zarrab, Reza Zwuo, Wang Salimans, Sanjeev Satheesh, Tim |
| author_facet | Weng, Chang Rood, Scott Ramezani, Mehdi Ali Aslani, Amir Zarrab, Reza Zwuo, Wang Salimans, Sanjeev Satheesh, Tim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15900 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities Weng, Chang Rood, Scott Ramezani, Mehdi Ali Aslani, Amir Zarrab, Reza Zwuo, Wang Salimans, Sanjeev Satheesh, Tim Computation and Language 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. |
| title | A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.15900 |