用深度卷积网络提升NLP的准确率与效率
A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities
- 将深度卷积网络融入NLP，结合生成对抗网络增强语言理解
- 在分词任务上准确率提升10%，召回率提高4%
- 适合需要高效文本处理的应用场景
自然语言处理广泛应用于机器翻译和情感分析等领域，但传统模型在准确率和效率方面存在瓶颈。本文引入深度卷积神经网络（DCNN），结合机器学习算法与生成对抗网络（GAN），提升语言理解能力，降低歧义，增强任务性能。实验表明，该高性能NLP模型在分词任务上的准确率提升10%，召回率增加4%。该集成方法在词性标注、机器翻译和文本分类等任务中表现优异，具备更高的识别精度与处理效率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。