Her research focuses on natural language processing and computational linguistics, specifically information extraction, sentiment analysis, semantic class induction, and bootstrapping methods that learn from unannotated texts.
After receiving her bachelor’s degree in applied mathematics (computer science) from Carnegie Mellon University, Riloff completed both her M.S.
and Ph.D. in Computer Science at the University of Massachusetts Amherst,[1] where she defended her dissertation under the guidance of Wendy Lehnert.
[3] Riloff’s primary research areas include information extraction, sentiment & affective text analysis, semantic class induction, social media analysis, coreference resolution, and medical text processing.
Riloff has also worked more broadly on coreference resolution, sentiment analysis, active learning, and even veterinary medicine.