Structured Learning for Taxonomy Induction with Belief Propagation
Mohit Bansal, David Burkett, Gerard de Melo, Dan Klein · 2014
We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation.Our model incorporates heterogeneous relational evidence about both hypernymy and siblinghood, captured by semantic features based on patterns and statistics from Web n-grams and Wikipedia abstracts.For efficient inference over taxonomy structures, we use loopy belief propagation along with a directed spanning tree algorithm for the core hypernymy factor.To train the system, we extract sub-structures of WordNet and discriminatively learn to reproduce them, using adaptive subgradient stochastic optimization.On the task of reproducing sub-hierarchies of WordNet, our approach achieves a 51% error reduction over a chance baseline, including a 15% error reduction due to the non-hypernym-factored sibling features.On a comparison setup, we find up to 29% relative error reduction over previous work on ancestor F1.