Prins, Robbert Paul

English

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

Dissertations of the University of Groningen

Finite-state pre-processing for natural language analysis

https://pure.rug.nl/ws/files/9802797/c2.pdf

Finite-state pre-processing for natural language analysis

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Dissertations of the University of Groningen