A statistical model for identifying grammatical relations in Korean sentences
This study aims to identify grammatical relations (GRs) in Korean sentences. The key task is to find the GRs in sentences in terms of such GR categories as subject, object, and adverbial. To overcome this problem, we are faced with the structural ambiguity and the granunatical relational ambiguity. We propose a statistical model, which resolves the grammatical relational ambiguity first, and then resolves structural ambiguity by using the probabilities of the GRs given noun phrases and verb phrases in sentences. The proposed model uses the characteristics of the Korean language such as distance, no-crossing and case property. We showed that consideration of such characteristics produces better results than without consideration by experiments. We attempt to enhance our system by estimating the probabilities of the proposed model with the Maximum Entropy (ME) model, and with Support Vector Machines (SVM) classifiers and we confirm that SVM classifiers improved the performance of our proposed model through experiments. Through an experiment with a tree and GR tagged corpus for training the model, we achieved an overall accuracy of 84.8%, 94.1 %, and 84.8% in identifying subject, object, and adverbial relations in sentences, respectively.
grammatical relations support vector machine shallow parsing backed-of method maximum entropy model