Training a Korean SRL System with Rich Morphological Features
Young‐Bum Kim, Heemoon Chae, Benjamin Snyder, Yu-Seop Kim · 2014
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.