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 text.
CITATION STYLE
Brill, E., & Pop, M. (1999). Unsupervised Learning of Disambiguation Rules for Part-of-Speech Tagging (pp. 27–42). https://doi.org/10.1007/978-94-017-2390-9_3
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