Abstract
In this paper we describe an unsupervised learning algorithm for automatically training a rule-based part of speech tagger without using a manually tagged corpus. We compare this algorithm to the Baum-Welch algorithm, used for unsupervised training of stochastic taggers. Next, we show a method for combining unsupervised and supervised rule-based training algorithms to create a highly accurate tagger using only a small amount of manually tagged text1.
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Brill, E., Pop, M. (1999). Unsupervised Learning of Disambiguation Rules for Part-of-Speech Tagging. In: Armstrong, S., Church, K., Isabelle, P., Manzi, S., Tzoukermann, E., Yarowsky, D. (eds) Natural Language Processing Using Very Large Corpora. Text, Speech and Language Technology, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-2390-9_3
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DOI: https://doi.org/10.1007/978-94-017-2390-9_3
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