Unsupervised Learning of Prototypical Fillers for Implicit Semantic Role Labeling

Abstract

Gold annotations for supervised implicit semantic role labeling are extremely sparse and costly. As a lightweight alternative, this paper describes an approach based on unsupervised parsing which can do without iSRL-specific training data: We induce prototypical roles from large amounts of explicit SRL annotations paired with their distributed word representations. An evaluation shows competitive performance with supervised methods on the SemEval 2010 data, and our method can easily be applied to predicates (or languages) for which no training annotations are available.