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The paper describes refinements that are currently being investigated in a model for part-of-speech assignment to words in unrestricted text. The model has the advantage that a pre-tagged training corpus is not required. Words are represented by equivalence classes to reduce the number of parameters required and provide an essentially vocabulary-independent model. State chains are used to model selective higher-order conditioning in the model, which obviates the proliferation of parameters attendant in uniformly higher-order models. The structure of the state chains is based on both an analysis of errors and linguistic knowledge. Examples show how word dependency across phrases can be modeled.
Julian Kupiec (Sun,) studied this question.