A novel disambiguation method for unification-based grammars using probabilistic context-free approximations

Authors:
Bernd Kiefer;Hans-Ulrich Krieger;Detlef Prescher
Affiliations:
Language Technology Lab, Saarbrücken, Germany;Language Technology Lab, Saarbrücken, Germany;Language Technology Lab, Saarbrücken, Germany
Venue:
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Year:
2002

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Abstract

We present a novel disambiguation method for unification-based grammars (UBGs). In contrast to other methods, our approach obviates the need for probability models on the UBG side in that it shifts the responsibility to simpler context-free models, indirectly obtained from the UBG. Our approach has three advantages: (i) training can be effectively done in practice, (ii) parsing and disambiguation of context-free readings requires only cubic time, and (iii) involved probability distributions are mathematically clean. In an experiment for a mid-size UBG, we show that our novel approach is feasible. Using unsupervised training, we achieve 88% accuracy on an exact-match task.