Accurate identification of temporal information such as date is crucial for advancing cancer research which often requires precise date information associated with related cancer events. However, there is a gap for existing natural language processing (NLP) systems to identify dates for specific cancer research studies. Illustrated with two case studies, we investigated the feasibility, evaluated the performances and discussed the challenges of date information extraction for cancer research.
Learning Objective: Audiences will learn feasibility and challenges of date information extraction for cancer research using natural language processing.
Liwei Wang (Presenter)
Jason A Wampfler, Mayo Clinic
Angela Angela Dispenzieri, Mayo Clinic
Hua Xu, University of Texas Health Science Center at Houston
Ping Yang, Mayo Clinic
Hongfang Liu, Mayo Clinic