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.
The World Wide Web contains vast quantities of textual information in several forms: unstructured text, template-based semi-structured webpages (which present data in key-value pairs and lists), and tables. Methods for extracting information from these sources and converting it to a structured form have been a target of research from the natural language processing (NLP), data mining, and database communities. While these researchers have largely separated extraction from web data into different problems based on the modality of the data, they have faced similar problems such as learning with limited labeled data, defining (or avoiding defining) ontologies, making use of prior knowledge, and scaling solutions to deal with the size of the Web.
This tutorial takes a holistic view toward information extraction, exploring the commonalities in the challenges and solutions developed to address these different forms of text. We will explore the approaches targeted at unstructured text that largely rely on learning syntactic or semantic textual patterns, approaches targeted at semi-structured documents that learn to identify structural patterns in the template, and approaches targeting web tables which rely heavily on entity linking and type information. Finally, we will look at recent research that takes a more inclusive approach toward textual extraction by combining the different signals from textual, layout, and visual clues into a single model made possible by deep learning methods.
Xin Luna Dong is a Principal Scientist at Amazon, leading the efforts of constructing Amazon Product Knowledge Graph. She was one of the major contributors to the Google Knowledge Vault project, and has led the Knowledge-based Trust project, which was called the "Google Truth Machine" by the Washington Post. She has co-authored the book "Big Data Integration", was awarded ACM Distinguished Member, VLDB Early Career Research Contribution Award for "advancing the state of the art of knowledge fusion", and Best Demo award in Sigmod 2005. She serves in VLDB endowment and PVLDB advisory committee, and is a PC co-chair for VLDB 2021, ICDE Industry 2019, VLDB Tutorial 2019, Sigmod 2018 and WAIM 2015. She has given multiple tutorials on data integration, graph mining, and knowledge management in top-tier conferences.
Colin Lockard is a PhD student at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he has published papers on knowledge extraction from both unstructured and semi-structured text.
Prashant Shiralkar is an Applied Scientist in the Product Graph team at Amazon. He currently works on knowledge extraction from semi-structured data. Previously, he received a Ph.D. from Indiana University Bloomington where his dissertation work focused on devising computational approaches for fact checking by mining knowledge graphs. His research interests include machine learning, data mining, information extraction and NLP, and Semantic Web technologies.
Hannaneh Hajishirzi is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She works on NLP, AI, and machine learning, particularly designing algorithms for semantic understanding, reasoning, question answering, and information extraction from multimodal data. She has earned numerous awards for her research, including an Allen Distinguished Investigator Award, a Google Faculty Research Award, a Bloomberg Data Science Award, an Amazon Research Award, and a SIGDIAL Best Paper Award.