Part-of-speech tagging using a Variable Memory Markov model

We present a new approach to disambiguating syntactically ambiguous words in context, based on Variable Memory Markov (VMM) models. In contrast to fixed-length Markov models, which predict based on fixed-lenth histories, variable memory Markov models dynamically adapt their history length based on the training data, and hence may use fewer parameters. In a test of a VMM based tagger on the Brown corpus, 95.81% of tokens are correctly classified.