LPNMR 2019 Workshops and Shared Sessions

Monday June 3rd
  • 09:00-10:00 TaPP keynote speaker: Val Tannen (title to be announced)
  • 10:00-10:30 TaPP talk (title to be announced)
  • 10:30-11:00 coffee break
  • 11:00-12:30 ASPOCP (titles to be announced)
  • 12:30-14:00 lunch (on your own)
  • 14:00-15:30 ASPOCP and NIST Workshop (titles to be announced)
  • 15:30-16:00 coffee break
  • 16:00-17:30 NIST Workshop (titles to be announced)
Tuesday June 4th
  • 9:00-10:00 BX invited speaker: Zachary Ives: Views, Update Propagation, and Provenance
  • 10:00-10:30 BX talk: Nils Weidmann, Anthony Anjorin, Lars Fritsche, Gergely Varró, Andy Schürr, and Erhan Leblebici. Incremental Bidirectional Model Transformation with eMoflon::IBeX
  • 10:30-11:00 coffee break
  • 11:00-12:30 CAUSAL (title to be announced)
  • 12:30-2:00 lunch (on your own)
  • 14:00-15:00 Datalog 2.0 tutorial: Francesco Ricca. An Extension of Datalog for Modelling and Solving Complex Combinatorial Problems
  • 15:00-15:30 Sahil Gupta, Yi-Yun Cheng, and Bertram Ludaescher. Possible Worlds Explorer: Datalog & Answer Set Programming for the Rest of Us
  • 15:30-16:00 coffee break
  • 16:00-17:00 Tutorial by Yuliya Lierler. Information Extraction Tool Text2Alm: From Narratives to Action Language System Descriptions
Yuliya Lierler, University of Nebraska Omaha, USA — Tutorial
Information Extraction Tool Text2Alm: From Narratives to Action Language System Descriptions
Abstract: The tutorial will explain the design of a narrative understanding tool Text2Alm. System Text2Alm uses an action language ALM to perform inferences on complex interactions of events described in narratives. The methodology used to implement the Text2Alm was originally outlined by Lierler, Inclezan, and Gelfond in 2017. Text2Alm relies on a conglomeration of resources and techniques from two distinct fields of artificial intelligence, namely, natural language processing and knowledge representation and reasoning. The tutorial will also present the results on the effectiveness of system Text2Alm measured by its ability to correctly answer questions from the bAbI tasks published by Facebook Research in 2015. This tool matched or exceeded the performance of state-of-the-art machine learning methods in six of the seven tested tasks.

Biography: Yuliya Lierler is an associate professor at the Computer Science Department at the University of Nebraska Omaha. Prior to coming to the University of Nebraska, Dr. Lierler was a Computing Innovation Fellow Postdoc at the University of Kentucky. She holds a Ph.D. in Computer Sciences from the University of Texas at Austin. Dr. Lierler’s research interests include the field of artificial intelligence, especially in the area of knowledge representation, automated reasoning, declarative problem solving, and natural language understanding.