Xin Luna Dong
ABSTRACT
How do we surface the large amount of information present in HTML documents on the Web, from news articles 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.
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Digital Library
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- Colin Lockard, Xin Luna Dong, Arash Einolghozati, and Prashant Shiralkar. 2018. CERES: Distantly supervised relation extraction from the semi-structured web. PVLDB 11, 10 (2018), 1084--1096.Google ScholarDigital Library
- Colin Lockard, Prashant Shiralkar, Xin Luna Dong, and Hannaneh Hajishirzi. 2020. ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages. In ACL. Association for Computational Linguistics, Online, 8105--8117. https: //www.aclweb.org/anthology/2020.acl-main.721Google Scholar
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Index Terms
- Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web
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