Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training

Details Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training

2000-08-30

arxiv.org/abs/cs/0008034v1

Proceedings of the 38th Annual Meeting of the ACL, 2000

Computer Science

Computation and Language

8 pages, uses acl2000.sty

Scientific paper

We present a new approach to stochastic modeling of constraint-based grammars that is based on log-linear models and uses EM for estimation from unannotated data. The techniques are applied to an LFG grammar for German. Evaluation on an exact match task yields 86% precision for an ambiguity rate of 5.4, and 90% precision on a subcat frame match for an ambiguity rate of 25. Experimental comparison to training from a parsebank shows a 10% gain from EM training. Also, a new class-based grammar lexicalization is presented, showing a 10% gain over unlexicalized models.

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