Title: Towards Deeper Natural Language Understanding
Abstract: Extracting meaning from text is key to natural language understanding and many end-user applications. Natural language is notoriously ambiguous, and humans intuitively understand many nuances in meaning as well as implicit inferences. In this talk, I will present models that enable intelligent systems to better understand natural language. The first project targets implicit positive meaning hidden in sentences containing negation. I will discuss approaches to pinpoint the few elements that are actually negated, and strategies to generate plausible affirmative counterparts. The second project targets changes in meaning over time. I will cover our work on extracting temporally-anchored spatial knowledge and track possession changes over time.
Bio: Eduardo Blanco is an Assistant Professor in the Department of Computer Science and Engineering at University of North Texas. He conducts research in natural language processing with a focus on computational semantics, semantic relation extraction and inference, and intricate linguistic phenomena such as negation, modality and uncertainty. His work is supported by the National Science Foundation, the Patient-Centered Outcomes Research Institute, and generous gifts from industry. Blanco is a recent recipient of the Bloomberg Data Science Research Grant and the National Science Foundation CAREER Award.