Paper: Global Learning of Focused Entailment Graphs

ACL ID P10-1124
Title Global Learning of Focused Entailment Graphs
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010

We propose a global algorithm for learn- ing entailment relations between predi- cates. We define a graph structure over predicates that represents entailment rela- tions as directed edges, and use a global transitivity constraint on the graph to learn the optimal set of edges, by formulating the optimization problem as an Integer Linear Program. We motivate this graph with an application that provides a hierar- chical summary for a set of propositions that focus on a target concept, and show that our global algorithm improves perfor- mance by more than 10% over baseline al- gorithms.