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.
For instance, REF explicitly search for the smallest model that explains the data using Integer Programming, and then use EM to set parameter values.
Minimized Models for Unsupervised Part-of-Speech Tagging
ACL-IJCNLP
Computer Science