Unsupervised Named Entity Classification Models and their Ensembles

This paper proposes an unsupervised learning model for classifying named entities. This model uses a training set, built automatically by means of a small-scale named entity dictionary and an unlabeled corpus. This enables us to classify named entities without the cost for building a large hand-tagged training corpus or a lot of rules. Our model uses the ensemble of three different learning methods and repeats the learning with new training examples generated through the ensemble learning. The ensemble of various learning methods brings a better result than each individual learning method. The experimental result shows 73.16% in precision and 72.98% in recall for Korean news articles.