Integrating Logical Representations with Probabilistic Information using Markov Logic (2011)
First-order logic provides a powerful and flexible mechanism for representing natural language semantics. However, it is an open question of how best to integrate it with uncertain, probabilistic knowledge, for example regarding word meaning. This paper describes the first steps of an approach to recasting first-order semantics into the probabilistic models that are part of Statistical Relational AI. Specifically, we show how Discourse Representation Structures can be combined with distributional models for word meaning inside a Markov Logic Network and used to successfully perform inferences that take advantage of logical concepts such as factivity as well as probabilistic information on word meaning in context.
In Proceedings of the International Conference on Computational Semantics, pp. 105--114, Oxford, England, January 2011.

Dan Garrette Ph.D. Alumni dhg [at] cs utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu