1989 · 20 citations · 13 references
ComputingComputer ScienceLinguisticsComputational LinguisticsKnowledge RepresentationNatural Language ProcessingDependency GrammarDependency ParsingHidden Markov ModelsLanguage ModelingLanguage ModelsPart-of-speech TaggingEquivalence ClassesFinite-state Markov ChainsHidden Markov Model TrainingHigher-order ConditioningHigher-order Markov ModelsMarkov Language ModelsPhrase TaggingSecond-order Conditioning
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.
13