Cascaded grammatical relation-driven parsing using Support Vector Machines

This study aims to identify dependency structure in Korean sentences with the cascaded chunking strategy. In the first stages of the cascade, we find chunks of NP and guess grammatical relations (GRs) using Support Vector Machine (SVM) classifiers for every possible modifier-head pairs of chunks in terms of GR categories as subject, object, complement, adverbial, and etc. In the next stage, we filter out incorrect modifier-head relations in each cascade for its corresponding GR using the SVM classifiers and the characteristics of the Korean language such as distance, no-crossing and case property. Through an experiment with a tree and GR tagged corpus for training the proposed parser, we achieved an overall accuracy of 85.7% on average.