Exploring the Effectiveness of Lexical Ontologies for Modeling Temporal Relations with Markov Logic

Temporal analysis of events is a central problem in computational models of discourse. However, correctly recognizing temporal aspects of events poses serious challenges. This paper introduces a joint modeling framework and feature set for temporal analysis of events that utilizes Markov Logic. The feature set includes novel features derived from lexical ontologies. An evaluation suggests that introducing lexical relation features improves the overall accuracy of temporal relation models.

• Type: Journal
• Year: 2010
• Where: SIGDIAL
• Authors: Eun Y. Ha, Alok Baikadi, Carlyle Licata, Bradford W. Mott, James C. Lester
• Keywords: Natural Language Processing, SIGDIAL 2010, Temporal Analysis, Temporal Aspects, Temporal Relation Models