Unstructured documents often come with embedded structured data. Representing valuable and structured information as tables is popular in health, financial, and many domains. However, manual extraction of structured information from documents typically costs tremendous time and labor, motivating the need for a system for automating the process. After such tables have been extracted, the data can be used for a wide variety of tasks such as question answering and various “down-stream” analytics tasks. In this talk, we will discuss how to leverage ground breaking pre-trained language models (e.g., BERT, ChatGPT) to develop tools for automated table extraction from various types of documents. We will present different applications from cancer registry reporting, cancer care, and psychiatry hospitalization prediction.
Professor Raymond Ng is the Canada Research Chair on data science and analytics. He is also the founding Director of the UBC Data Science Institute, and an elected fellow of the Royal Society of Canada. For both 2022 and 2023, he was named one of the world’s top-75 academic data science leaders by the MIT-based CDO magazine. Professor Raymond Ng’s main research area for the past three decades is on data mining, with a specific focus on health informatics text mining, and Natural Language Processing. He has published over 230 peer-reviewed publications on data clustering, outlier detection, OLAP processing, health informatics and text mining. He is the recipient of two best paper awards – from the 2001 ACM SIGKDD conference, the premier data mining conference in the world, and the 2005 ACM SIGMOD conference, one of the top database conferences worldwide. For the past decade, he has co-led several large-scale genomic projects funded by Genome Canada, Genome BC and industrial collaborators. (H-index 72; total citations 37,000+)
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
