Understanding Convolutional Neural Networks for NLP

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已于 2023-08-18 15:03:04 修改

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分类专栏： MCP实战开发AI大模型应用与大数据计算架构深度学习实战文章标签：自然语言处理人工智能语言模型编程实践开发语言架构设计

于 2023-08-18 11:19:36 首次发布

本文链接：https://blog.youkuaiyun.com/universsky2015/article/details/132358121

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本文探讨了如何在自然语言处理（NLP）任务中应用卷积神经网络（CNN），特别是在情感分析中的应用。文章详细介绍了CNN如何用于从文本数据中提取复杂模式和特征，并使用IMDB电影评论数据集进行示例。内容涵盖模型结构、数据处理流程、参数设置，以及模型的实现、训练和评估。

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作者：禅与计算机程序设计艺术

1.简介

Convolutional Neural Network (CNN) has been widely used in Natural Language Processing (NLP) tasks due to its ability to capture complex patterns and features from text data, which makes it suitable for analyzing long sequences of words or characters such as sentences or paragraphs. This article presents an exploration into CNNs applied specifically to sentiment analysis problems in NLP by using the popular IMDB dataset that contains movie reviews labeled as positive or negative. The main focus of this article is to explain how convolutional layers are applied to extract relevant features from text data before feeding them to fully connected layers for classification. The implementation details are also discussed alongside key