Abstract
A simple, robust sliding-window part-of-speech tagger is presented and a method is given to estimate its parameters from an untagged corpus. Its performance is compared to a standard Baum-Welchtrained hidden-Markov-model part-of-speech tagger. Transformation into a finite-state machine-behaving exactly as the tagger itself- is demonstrated.
Cite
CITATION STYLE
Sánchez-Villamil, E., Forcada, M. L., & Carrasco, R. C. (2004). Unsupervised training of a finite-state sliding-window part-of-speech tagger. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3230, pp. 454–463). Springer Verlag. https://doi.org/10.1007/978-3-540-30228-5_40
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