用双向LSTM与注意力机制提升新闻分类准确率与效率
Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing
- 结合双向LSTM与注意力机制，自动提取新闻文本关键特征
- 实验表明分类准确率显著提升，人工干预需求大幅减少
- 适合需要高效处理海量新闻数据的媒体与信息平台
互联网技术的发展导致新闻信息量迅速增加。从复杂信息中筛选有价值内容已成为亟需解决的问题。针对传统人工分类方法耗时低效的不足，本文提出一种基于深度学习的新闻文本自动分类方案。该方案通过引入先进的机器学习算法，尤其是结合双向长短期记忆网络（Bi-LSTM）与注意力机制的优化模型，实现新闻文本的高效分类与管理。实验结果表明，该方案不仅能显著提高分类的准确率与及时性，还能大幅降低对人工干预的需求。通过对多种常见模型的对比分析，验证了所提方法的有效性与先进性，为未来新闻文本分类研究奠定了坚实基础。
原文摘要 · Abstract (English)
The development of Internet technology has led to a rapid increase in news information. Filtering out valuable content from complex information has become an urgentproblem that needs to be solved. In view of the shortcomings of traditional manual classification methods that are time-consuming and inefficient, this paper proposes an automaticclassification scheme for news texts based on deep learning. This solution achieves efficient classification and management of news texts by introducing advanced machine learning algorithms, especially an optimization model that combines Bi-directional Long Short-Term Memory Network (Bi-LSTM) and Attention Mechanism. Experimental results show that this solution can not only significantly improve the accuracy and timeliness of classification, but also significantly reduce the need for manual intervention. It has important practical significance for improving the information processing capabilities of the news industry and accelerating the speed of information flow. Through comparative analysis of multiple common models, the effectiveness and advancement of the proposed method are proved, laying a solid foundation for future news text classification research.
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。