PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Entailment Relations by Global Graph Structure Optimization
Jonathan Berant, Jacob Goldberger and Ido Dagan
Computational Linguistics Volume 38, Number 1, 2012.

Abstract

Identifying entailment relations between predicates is an important part of applied semantic inference. In this article we propose a global inference algorithm that learns such entailment rules. First, we define a graph structure over predicates that represents entailment relations as directed edges. Then, we use a global transitivity constraint on the graph to learn the optimal set of edges, formulating the optimization problem as an Integer Linear Program. The algorithm is applied in a setting where, given a target concept, the algorithm learns on-the-fly all entailment rules between predicates that co-occur with this concept. Results show that our global algorithm improves performance over baseline algorithms by more than 10%.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8760
Deposited By:Jonathan Berant
Deposited On:21 February 2012