Title: Building Syntactic-Semantic Inference Rules from Text

Speaker: Prachi Jain

Abstract: The visions of machine reading and deep language understanding emphasize the ability to draw inferences from the text to discover implicit information that may not be explicitly stated. Example: If (X, got married to, Y) is true then (X, is a relative of, Y) is also true. This has natural applications to textual entailment, knowledge-base (KB) completion, and effective querying over KBs. One popular approach for such inference use inference rules.

The objective, in this talk, is to get a hitchhiker's view of the evolving techniques to extract inference rules from the text. We will talk about how syntactic structures, lexicographic resources (like WordNet, thesaurus) and distributional similarity can exploited to build a corpus of inference rules by looking at systems like - SHERLOCK, PATTY, CLEAN, VCLEAN, PPDB 2.0, NaturalLI. Then we will look at some outstanding challenges which are essential for progress in the field of semantic inference and hence in Natural Language Understanding.