Probabilistic Normalization and Unpacking of Packed Parse Forests for Unification-based Grammars

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The research described below forms part of a wider programme to develop a practical parser for naturally-occurring natural language input which is capable of returning the n-best syntactically-determinate analyses, containing that which is semantically and pragmatically most appropriate (preferably as the highest ranked) from the exponential (in sentence length) syntactically legitimate possibilities (Church and Patil 1983), which can frequently run into the thousands with realistic sentences and grammars. We have opted to develop a domain-independent solution to this problem based on integrating statistical Markov modelling techniques, which offer the potential for rapid tuning to different sublanguages / corpora on the basis of supervised training, with linguistically-adequate grammatical (language) models, capable of returning analyses detailed enough to support semantic interpretation.