Unsupervised Semantic Role Labeling for Korean Adverbial Case
김병수, 이용훈, Jong-Hyeok Lee · Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong · 2007
Training a statistical model for semantic role labeling requires a large amount of manually tagged corpus. However. such corpus does not exist for Korean and constructing one from scratch is a very long and tedious job. This paper suggests a modified algorithm of self-training, an unsupervised algorithm, which trains a semantic role labeling model from any raw corpora. For initial training, a small tagged corpus is automatically constructed iron case frames in Sejong Electronic Dictionary. Using the corpus, a probabilistic model is trained incrementally, which achieves 83.00% of accuracy in 4 selected adverbial cases.