This material is based upon work supported by the National Science Foundation under Grant No. 1523637.
Tasks in natural language semantics are requiring increasingly complex and fine-grained inferences. This project pursues the dual hypotheses that (a) logical form is best suited for supporting such inferences, and that (b) it is necessary to reason explicitly about uncertain, probabilistic information at the lexical level. This project combines logical form representations of sentence meaning with weighted inference rules derived from distributional similarity. It uses Markov Logic Networks for probabilistic inference over logical form with weighted rules, testing on the task of Recognizing Textual Entailment. It also develops new methods for describing word meaning in context distributionally in a way that is amenable to determining lexical entailment.