One noteworthy feature of our method is that there is no need to handle sparse and high-dimensional feature spaces often used in many supervised NLP systems, which is one of the main causes of the data sparseness problem often encountered when we learn the model with a supervised leaning algorithm.
nLP systems that are both compact and highperformance can be built by retraining the model with the obtained condensed feature set H. Let us first define the condensed feature set H. In this paper, we call the feature set generally used in supervised learning, F, the original feature set.