A simple, robust sliding-window part-of-speech tagger is presented and a method is given to estimate its parameters from an un- tagged corpus. Its performance is compared to a standard Baum-Welch- trained hidden-Markov-model part-of-speech tagger. Transformation into a finite-state machine —behaving exactly as the tagger itself— is demon- strated.
['Enrique Sánchez-Villamil', 'Mikel L. Forcada', 'Rafael C. Carrasco']
Unsupervised Training of a Finite-State Sliding-Window Part-of-Speech Tagger