由人工智能驱动的网络抓取用于从报纸中提取新闻内容的语义提取
Saravanan S1, Ashfauk Ahamed A K2
1Department of Computer Science and Engineering, B.S.Abdur Rahman Crescent Institute of Science & Technology, Chennai, 600 048, India.
Scientific reports
|November 25, 2025
概括
本研究介绍了WISE,这是一种用于从报纸档案中智能提取网络数据的深度学习框架. 通过语义理解和自适应抓取,WISE可提高35%的数据检索准确度和40%的效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 传统的网络爬虫在报纸档案中难以处理各种数据格式.
- 基于规则和关键字的方法缺乏适应性和语义理解.
- 有效的数据提取对媒体,研究和新闻工作至关重要.
研究的目的:
- 开发一个智能框架,从动态报纸数据库中高效准确地提取数据.
- 克服传统的网络爬虫在处理异构的网络内容方面的局限性.
- 通过整合自然语言处理和深度学习来改善数据检索.
主要方法:
- 提出了WISE (Web-Intelligent Semantic Extractor),这是一个基于深度学习的框架.
- 综合自然语言处理 (NLP) 和神经网络用于语义理解.
- 开发了基于内容语义和学习模式的动态抓取策略.
主要成果:
- 与传统爬虫相比,WISE的提取精度提高了35%,加工效率提高了40%.
- 证明了卓越的可扩展性,上下文准确性,语义理解和实时灵活性.
- 在从异质来源提取结构化数据方面表现出卓越的表现.
结论:
- WISE为现代网络数据提取挑战提供了一种新的智能解决方案.
- 该框架显著提高了从报纸数据库中检索信息的准确性和效率.
- WISE提供了一种可扩展,安全和适应的网络智能化方法.
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