Montague Meets Markov: Deep Semantics with Probabilistic Logical Form (2013)
I. Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney
We combine logical and distributional representations of natural language meaning by transforming distributional similarity judgments into weighted inference rules using Markov Logic Networks (MLNs). We show that this framework supports both judging sentence similarity and recognizing textual entailment by appropriately adapting the MLN implementation of logical connectives. We also show that distributional phrase similarity, used as textual inference rules created on the fly, improves its performance.
Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*Sem-2013) (2013), pp. 11--21.

Slides (PPT)
I. Beltagy Ph.D. Alumni beltagy [at] cs utexas edu
Cuong Kim Chau Formerly affiliated Ph.D. Student ckcuong [at] cs utexas edu
Dan Garrette Ph.D. Alumni dhg [at] cs utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu