Incorporating second-order information into two-step major phrase break prediction for Korean

In this paper, we present a new phrase break prediction method that integrates second-order information into general maximum entropy model. The phrase break prediction problem was mapped into a classification problem in our research. The features we used for the prediction of phrase breaks are of several layers such as local features (part-of-speech (POS) tags, a lexicon, lengths of eojeols and location of juncture in the sentence), global features (chunk label derived from a eojeol parse tree) and second-order features (distance probability of previous and next phrase break). These three features were combined and used in the experiments, and we were able to generate good performance especially in the major phrase break prediction.