earticle

영어

Semantic role labeling is typically used to resolve a problem from the perspective of classification by using a corpus. Semantic Role Labeling determines an adequate predicate-argument relation. It can be used to improve performance in various areas of natural language processing. In this paper, automatic semantic role labeling using 10,000 sentences in a semantic role tagged corpus constructed from a Korean syntax tagged corpus was conducted. In Korean, affix, such as josa and eomi, is a very important role in syntactic parsing and semantic role labeling. Semantic role labeling was achieved in this study by improving particle and word ending information, which were insufficiently addressed in previous studies on semantic role labeling, and creating new features. When features based on the affix information created in this study were added to the basic features used in previous studies on semantic role labeling of languages, an F1 score of approximately 80.83% was obtained.