Finite-state pre-processing for natural language analysis

Research output: ThesisThesis fully internal (DIV)

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Wide-coverage natural language parsers are typically not very efficient. Finite-state techniques are less powerful, but offer the advantage of being very fast, and good at representing language locally. This dissertation constitutes empirical research into the construction and use of a finite-state approximation of a wide-coverage parser to increase parsing performance. The finite-state approximation is in the form of a hidden Markov model, inferred from parser-annotated data. This model is used in a part-of-speech tagger, which is applied in various ways and using several different models to reduce ambiguity in parsing, by setting it up as a filter that removes unlikely options in the first stage of parsing.
Original languageEnglish
QualificationDoctor of Philosophy
  • Nerbonne, John, Supervisor
  • van Noord, Gertjan, Co-supervisor
Award date12-May-2005
Publication statusPublished - 2005


  • 17.46 mathematische linguïstiek, computerlinguïstiek
  • Proefschriften (vorm)
  • Natuurlijke-taalverwerking

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