Korean Semantic Role Labeling Using Korean PropBank Frame Files

Semantic role labeling (SRL) is a process that determines the semantic relation of a predicate and its arguments in a sentence and is an important factor in the semantic analysis of natural language processing. In this research, we propose a method for automatic SRL using frame files included in the Korean version of Proposition Bank (PropBank). First, we select the proper sense of the predicate among multiple senses. Senses of the predicate are classified according to the semantic and syntactic properties of its arguments. The semantic similarities between the noun words from the example sentence of each sense in the frame file and the nouns in the given sentence are measured. Finally, the sense with the highest similarity value is selected and the frame information of the sense is applied to the SRL. We acquired about 90% of accuracy.