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.
In Reference REF , an unsupervised learning algorithm is proposed for automatically training a rule-based POS tagger.
Unsupervised Learning Of Disambiguation Rules For Part Of Speech Tagging
Very Large Corpora
Computer Science