AI 中文总结
该研究以二战数字收藏为案例，评估三种自然语言处理方法用于众包集合关键词提取，涵盖多种人工智能技术。结果显示各方法有潜力但无完整方案，模型影响结果，还指出自动提取关键词有管理责任，开放式模型较优，生成式人工智能有风险。
AI 中文摘要
大规模识别和分配关键词对众包集合来说是一项技术、实践和伦理挑战。本文报告了“从众包集合中提取关键词”项目的发现，该项目以牛津大学托管的众包二战数字收藏“最辉煌时刻在线档案”为案例研究。项目评估了三种自然语言处理方法来自动提取关键词：命名实体识别、关键词提取和主题建模。它在从传统统计方法到现代生成式人工智能神经网络等一系列人工智能技术中测试了这些方法。我们的定量和定性研究结果表明，自然语言处理方法在众包集合中大规模提取关键词方面具有真正潜力，但没有单一方法能提供完整解决方案，模型选择对结果有显著影响。我们认为，在众包集合中，元数据是与在世贡献者互动的直接产物，自动关键词提取带来了独特的管理责任，必须在关注技术性能的同时加以解决。开放式、提取式模型在我们的评估中最适合支持负责任的部署，而生成式人工智能尽管有抽象潜力，但会带来问责风险，任何管理众包集合的人都应仔细权衡。
英文摘要
Identifying and assigning keywords at scale is a technical, practical, and ethical challenge for crowdsourced collections. This article reports the findings of the "Extracting Keywords from Crowdsourced Collections" project, which used the Their Finest Hour Online Archive, a crowdsourced Second World War digital collection hosted by the University of Oxford, as a case study. The project evaluated three Natural Language Processing approaches to automate keyword extraction: Named Entity Recognition, Keyword Extraction, and Topic Modelling. It tested these approaches across a range of artificial intelligence techniques, from traditional statistical methods to modern GenAI neural networks. Our quantitative and qualitative findings indicate that Natural Language Processing approaches offer real potential for keyword extraction at scale in crowdsourced collections, but that no single method offers a complete solution and that model choice significantly shapes results. We argue that in crowdsourced collections, where metadata is the direct product of engagement with living contributors, automated keyword extraction raises distinct stewardship responsibilities that must be addressed alongside technical performance. Open-weight, extractive models emerge from our evaluation as best placed to support responsible deployment, while generative AI, despite its abstractive potential, introduces accountability risks that anyone managing crowdsourced collections should weigh carefully.
Comments45 pages, 6 tables
