Talk Title: Towards Better Informed Extraction of Events from Documents
Talk Abstract: Large amounts of text are written and published daily on-line. As a result, applications such as reading through the document to automatically extract useful information, and answering user questions have become increasingly needed for people’s efﬁcient absorption of information. In this talk, I will focus on the problem of finding and organizing information about events and introduce my recent research on document-level event extraction. Firstly, I’ll briefly summarize the high-level goal and several key challenges (including modeling context and better leveraging background knowledge), as well as my efforts to tackle them. Then I will focus on the work where we formulate event extraction as a question answering problem — both to access relevant knowledge encoded in large models and to reduce the cost of human annotation required for training data creation/construction.
Bio: Xinya Du is a Ph.D. candidate at the Computer Science Department of Cornell University, advised by Prof. Claire Cardie. He received a bachelor degree in Computer Science from Shanghai Jiao Tong University. His research is on natural language processing, especially methods that enable learning with fewer annotations for document-level information extraction. His work has been published in leading NLP conferences and has been covered by New Scientist and TechRepublic.