Predicting Phrase-Level Tags Using Entropy Inspired Discriminative Models

In this paper, we describe a system which predicts phrase-level tags for eojeols in Korean using entropy inspired discriminative probabilistic models such as a conditional random fields. Instead of selecting features by the intuition of user, we use a decision tree and error analysis systematically for selecting the best feature. Once we generate all available features from the corpus, then select features by using decision tree and error analysis iteratively. Experimental results show 93.90% and 49.46% accuracy for eojeols and sentences respectively. This accuracy eventually is able to improve further syntactic analysis results. We find from the results that the better meaningful features using systematic methods is good at raising performance.