Information extraction can support novel and effective access paths for digital libraries. Nevertheless, designing reliable extraction workflows can be cost-intensive in practice. On the one hand, suitable extraction methods rely on domain-specific training data. On the other hand, unsupervised and open extraction methods usually produce not-canonicalized extraction results. This paper tackles the question how digital libraries can handle such extractions and if their quality is sufficient in practice. We focus on unsupervised extraction workflows by analyzing them in case studies in the domains of encyclopedias (Wikipedia), pharmacy and political sciences. We report on opportunities and limitations. Finally we discuss best practices for unsupervised extraction workflows.
翻译：信息提取可支持数字图书馆的新颖而有效的访问路径。然而,设计可靠的提取工作流程在实践上可能耗费大量费用。一方面,适当的提取方法依赖于特定领域的培训数据。另一方面,未经监督和开放的提取方法通常产生非摄取性提取结果。本文探讨了数字图书馆如何处理此类提取以及其质量在实践上是否足够的问题。我们侧重于未经监督的提取工作流程,在百科全书(维基佩迪亚)、药房和政治科学领域的案例研究中分析这些流程。我们报告了机会和局限性。我们最后讨论了未经监督的提取工作流程的最佳做法。
相关内容
微信扫码咨询专知VIP会员