Knowledge Representation and Natural Language Understanding Lab

Research Topics

Knowledge representation, natural language understanding, automated reasoning, declarative problem solving. We are particularly interested in applying automated reasoning techniques for solving inference problems stemming from natural language understanding domain. Also, our work spans theoretic foundations as well as practical implementations of methods for automated reasoning based on model generation.

Current Lab Members Dr. Yuliya Lierler Dr. Jorge Fandinno Daniel Bresnahan Zachary Hansen Joel Sare Eleuterio Juan Lillo Portero
2019 ICLP Academic Family Reunion

Research Projects

• Automated optimization of programs and processing tools in answer set programming
• Modularity for modeling and solving in declarative programming
• Answer set programming and solving
• Constraint answer set programming
• Semantic-based Information Extraction System from Textual Input
• What lexical wide coverage resources can do for parsing and Information Extraction
• Textual inference
• Causality in natural language understanding
• Syntactic parsing by means of declarative programming

ZOOM-based Seminars

Seminars on Answer Set Programming hosted by the lab are linked here. If your are interested joining this ZOOM-based seminar send an email to aspseminar-join@lists.unomaha.edu with the word "subscribe" in the subject line.