Paper: Unsupervised Induction of Semantic Roles

ACL ID N10-1137
Title Unsupervised Induction of Semantic Roles
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

Datasets annotated with semantic roles are an important prerequisite to developing high- performance role labeling systems. Unfortu- nately, the reliance on manual annotations, which are both difficult and highly expen- sive to produce, presents a major obstacle to the widespread application of these systems across different languages and text genres. In this paper we describe a method for induc- ing the semantic roles of verbal arguments di- rectly from unannotated text. We formulate the role induction problem as one of detecting alternations and finding a canonical syntactic form for them. Both steps are implemented in a novel probabilistic model, a latent-variable variant of the logistic classifier. Our method increases the purity of the induced role clus- ters by a wide margin over a s...