Tagging English text with a probabilistic model
Bernard Mérialdo · 1994
In this paper we present some experiments on the use of a probabilistic model to tag English text, i.e. to assign to eachword the correct tag #part of speech# in the context of the sentence. The main novelty of these experiments is the use of untagged text in the training of the model. Wehave used a simple triclass Markov model and are looking for the best way to estimate the parameters of this model, depending on the kind and amount of training data provided. Two approaches in particular are compared and combined: to use text that has been tagged by hand and compute relative frequency counts, to use text without tags and train the model as a hidden Markov process, according to a Maximum Likelihood principle.