Web-scale Knowledge Collection

Colin Lockard, Prashant Shiralkar, Xin Dong, Hannaneh Hajishirzi
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引用次数: 3

Abstract

How do we surface the large amount of information present in HTML documents on the Web, from news articles to scientific papers to Rotten Tomatoes pages to tables of sports scores? Such information can enable a variety of applications including knowledge base construction, question answering, recommendation, and more. In this tutorial, we present approaches for Information Extraction (IE) from Web data that can be differentiated along two key dimensions: 1) the diversity in data modality that is leveraged, e.g. text, visual, XML/HTML, and 2) the thrust to develop scalable approaches with zero to limited human supervision. We cover the key ideas and intuition behind existing approaches to emphasize their applicability and potential in various settings.
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网络规模的知识收集
我们如何在Web上呈现HTML文档中的大量信息,从新闻文章到科学论文,从烂番茄页面到体育比分表?这些信息可以支持各种应用程序,包括知识库构建、问题回答、推荐等等。在本教程中,我们介绍了从Web数据中提取信息(IE)的方法,这些方法可以根据两个关键维度进行区分:1)所利用的数据模式的多样性,例如文本、可视化、XML/HTML,以及2)开发零到有限人工监督的可扩展方法的动力。我们涵盖了现有方法背后的关键思想和直觉,以强调它们在各种环境中的适用性和潜力。
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