Recently, most part-of-speech tagging approaches, such as
rule-based, probabilistic and neural network approaches, have shown
very promising results. In this paper, we are particularly interested
in probabilistic approaches, which usually require lots of training
data to get reliable probabilities. We alleviate such a restriction of
probabilistic approaches by introducing a fuzzy network model to
provide a method for estimating more reliable parameters of a model
under a small amount of training data. Experiments with the Brown
corpus show that the performance of the fuzzy network model is much
better than that of the hidden Markov model under a limited amount of
training data.