Training a Korean SRL system with rich morphological features

Abstract

In this paper we introduce a semantic role labeler for Korean, an agglutinative language with rich morphology. First, we create a novel training source by semantically annotating a Korean corpus containing fine-grained morphological and syntactic information. We then develop a supervised SRL model by leveraging morphological features of Korean that tend to correspond with semantic roles. Our model also employs a variety of latent morpheme representations induced from a larger body of unannotated Korean text. These elements lead to state-of-the-art performance of 81.07% labeled F1, representing the best SRL performance reported to date for an agglutinative language. © 2014 Association for Computational Linguistics.