Abstract

We derive from first principles the basic equations for a few of the basic hidden-Markov-model word taggers as well as equations for other models which may be novel (the descriptions in previous papers being too spare to be sure). We give performance results for all of the models. The results from our best model (96.45% on an unused test sample from the Brown corpus with 181 distinct tags) is on the upper edge of reported results. We also hope these results clear up some confusion in the literature about the best equations to use. However, the major purpose of this paper is to show how the equations for a variety of models may be derived and thus encourage future authors to give the equations for their model and the derivations thereof. Introduction The last few years have seen a fair number of papers on part-of-speech tagging --- assigning the correct part of speech to each word in a text [1,2,4,5,7,8,9,10]. Most of these systems view the text as having been produced by a hidden Mar...

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