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Towards Deeper Natural Language Understanding with Eduardo Blanco
Presenter: Eduardo Blanco
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.