Paper: Structured Learning for Taxonomy Induction with Belief Propagation

ACL ID P14-1098
Title Structured Learning for Taxonomy Induction with Belief Propagation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation. Our model incorporates heterogeneous re- lational evidence about both hypernymy and siblinghood, captured by semantic features based on patterns and statistics from Web n-grams and Wikipedia ab- stracts. For efficient inference over tax- onomy structures, we use loopy belief propagation along with a directed span- ning tree algorithm for the core hyper- nymy factor. To train the system, we ex- tract sub-structures of WordNet and dis- criminatively learn to reproduce them, us- ing adaptive subgradient stochastic opti- mization. On the task of reproducing sub-hierarchies of WordNet, our approach achieves a 51% error reduction over a chance baseline, including a 15% err...