Paper: Lexicalized Stochastic Modeling Of Constraint-Based Grammars Using Log-Linear Measures And EM Training

ACL ID P00-1061
Title Lexicalized Stochastic Modeling Of Constraint-Based Grammars Using Log-Linear Measures And EM Training
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2000
Authors

We present a new approach to stochastic modeling of constraint- based grammars that is based on log- linear models and uses EM for esti- mation from unannotated data. The techniques are applied to an LFG grammar for German. Evaluation on an exact match task yields 86% pre- cision for an ambiguity rate of 5.4, and 90% precision on a subcat frame match for an ambiguity rate of 25. Experimental comparison to train- ing 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.