Paper: A Bayesian Approach to Unsupervised Semantic Role Induction

ACL ID E12-1003
Title A Bayesian Approach to Unsupervised Semantic Role Induction
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

We introduce two Bayesian models for un- supervised semantic role labeling (SRL) task. The models treat SRL as clustering of syntactic signatures of arguments with clusters corresponding to semantic roles. The first model induces these clusterings independently for each predicate, exploit- ing the Chinese Restaurant Process (CRP) as a prior. In a more refined hierarchical model, we inject the intuition that the clus- terings are similar across different predi- cates, even though they are not necessar- ily identical. This intuition is encoded as a distance-dependent CRP with a distance between two syntactic signatures indicating how likely they are to correspond to a single semantic role. These distances are automat- ically induced within the model and shared across predicates. Both models ...