Minimized models for unsupervised part-of-speech tagging

A novel method is described that uses integer programming to explicitly search for the smallest model that explains the data, and then uses EM to set parameter values, and performs better than existing state-of-the-art systems in both settings.

Abstract

We describe a novel method for the task of unsupervised POS tagging with a dictionary, one that uses integer programming to explicitly search for the smallest model that explains the data, and then uses EM to set parameter values. We evaluate our method on a standard test corpus using different standard tagsets (a 45-tagset as well as a smaller 17-tagset), and show that our approach performs better than existing state-of-the-art systems in both settings. 1