Research Papers 论文研究 5h ago Updated 2h ago 更新于 2小时前 46

Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora 大型网络爬取语料库中鲁棒且可扩展的文本包含检测

FindMyText is an open-source Python package designed for robust and scalable detection of text containment within large corpora. It utilizes a novel mechanism that captures sequences of matching document fingerprints to identify near-verbatim copies rather than just general textual similarity. The system leverages a distributed, disk-based indexing framework, enabling it to scale effectively to massive web-crawled datasets. Benchmarks across ArXiv, Wikipedia, and generic web content demonstrate 发布开源Python工具FindMyText,用于在大型语料库中高效检测文本的包含关系(部分或全部)。 创新性地通过捕获匹配指纹序列链,区分近verbatim复制与一般文本相似性,提升版权验证可靠性。 采用分布式磁盘索引框架,具备处理大规模网络爬取数据集的扩展能力。 在ArXiv、Wikipedia及通用网页内容三个新基准数据集上,性能优于现有替代方法。

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Hot 热度
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Quality 质量
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Impact 影响力

Analysis 深度分析

TL;DR

  • FindMyText is an open-source Python package designed for robust and scalable detection of text containment within large corpora.
  • It utilizes a novel mechanism that captures sequences of matching document fingerprints to identify near-verbatim copies rather than just general textual similarity.
  • The system leverages a distributed, disk-based indexing framework, enabling it to scale effectively to massive web-crawled datasets.
  • Benchmarks across ArXiv, Wikipedia, and generic web content demonstrate that FindMyText outperforms existing alternative approaches in accuracy and efficiency.

Why It Matters

This development provides a critical tool for copyright verification and plagiarism detection, addressing the growing need to monitor large-scale digital content for unauthorized use. For AI practitioners and researchers, it offers a scalable solution to ensure compliance and integrity when working with extensive web-crawled datasets.

Technical Details

  • Core Mechanism: Extends traditional document fingerprinting by explicitly capturing chains of matching fingerprints, allowing for reliable identification of near-verbatim text segments.
  • Scalability Architecture: Employs a distributed, disk-based indexing framework to handle the computational and storage demands of large web-crawled corpora.
  • Performance Validation: Evaluated against a new benchmark dataset, showing superior performance compared to other methods across diverse sources including academic papers (ArXiv), encyclopedic content (Wikipedia), and general web text.
  • Implementation: Delivered as an open-source Python package, facilitating integration into existing data processing pipelines.

Industry Insight

  • Organizations managing large-scale data ingestion should adopt robust containment detection tools like FindMyText to mitigate legal risks associated with copyrighted material.
  • The shift from simple similarity metrics to chain-based fingerprinting represents a significant improvement in precision for automated content moderation systems.
  • As web crawls grow larger, disk-based distributed indexing will become a standard requirement for real-time or near-real-time text verification tasks.

TL;DR

  • 发布开源Python工具FindMyText,用于在大型语料库中高效检测文本的包含关系(部分或全部)。
  • 创新性地通过捕获匹配指纹序列链,区分近verbatim复制与一般文本相似性,提升版权验证可靠性。
  • 采用分布式磁盘索引框架,具备处理大规模网络爬取数据集的扩展能力。
  • 在ArXiv、Wikipedia及通用网页内容三个新基准数据集上,性能优于现有替代方法。

为什么值得看

该工具为AI训练数据的版权合规审查提供了高效的技术手段,有助于解决大规模预训练数据中的侵权风险问题。其针对“近verbatim”复制的检测机制,比传统相似度算法更精准,对数据清洗和内容安全领域具有重要参考价值。

技术解析

  • 核心算法:基于文档指纹技术,但引入了新颖机制以显式捕捉匹配指纹的序列链,从而更可靠地识别近乎逐字复制的内容,而非仅依赖局部相似性。
  • 系统架构:利用分布式、基于磁盘的索引框架,确保系统能够扩展到处理海量网络爬取数据集,兼顾效率与存储需求。
  • 基准评估:构建了新的文本包含检测方法基准测试,并在ArXiv论文、维基百科和通用网页内容三个代表性数据集上进行了验证,结果显示FindMyText表现最佳。
  • 开源属性:作为开源Python包发布,便于社区集成到现有的数据处理流水线中,促进技术透明度和复用。

行业启示

  • 数据合规自动化:随着大模型训练数据规模激增,自动化工具在识别和过滤潜在侵权内容方面变得至关重要,企业应关注此类技术以降低法律风险。
  • 从相似性到精确匹配:传统的文本相似度度量在版权保护场景中存在局限,行业需转向能识别结构性复制和序列一致性的更精细检测技术。
  • 可扩展性是关键:处理TB级甚至PB级的非结构化文本数据时,系统的扩展性和资源管理(如分布式磁盘索引)是决定技术落地可行性的核心因素。

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