Dr. Porter's research and teaching focuses on machine reading. This technology holds tremendous potential for capturing knowledge for automated inference, question answering, explanation generation, and other AI capabilities that interest him.
Porter's research addresses the grand challenge in Artificial Intelligence of building knowledge bases containing the accumulated understanding of entire fields of human inquiry, such as cell biology or global warming.
Porter, B., Barker, K., and Kim, D.. 2010. "Improving the Quality of Text Understanding by Delaying Ambiguity Resolution". 23rd International Conference on Computational Linguistics.
Porter, B. with Barker, K., et.al.. 2007. "Learning by Reading: A Prototype System, Performance Baseline and Lessons Learned". Proceedings of the Twenty-Second National Conference on Artificial Intelligence.
Porter, B. with Barker, K., et.al.. 2004. "A Question-Answering System for AP Chemistry: Assessing KRR Technologies". The Ninth International Conference on the Principles of Knowledge Representation and Reasoning.
Porter, B., Barker, K., and Clark, P.. 21-23 October 2001. "A Library of Generic Concepts for Composing Knowledge Bases". First International Conference on Knowledge Capture.
Porter, B. and Clark, P.. 1997. "Building Concept Representations from Components". Proceedings of the National Conference on Artificial Intelligence.
Awards & Honors
President’s Associates Teaching Excellence Award, University of Texas at Austin
College of Natural Sciences Teaching Excellence Award
Best Paper Award, National Conference on Artificial Intelligence
Presidential Young Investigator, National Science Foundation