Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training
Computation and Language
2007-05-23 v1
Abstract
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.
Cite
@article{arxiv.cs/0008034,
title = {Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training},
author = {Stefan Riezler and Detlef Prescher and Jonas Kuhn and Mark Johnson},
journal= {arXiv preprint arXiv:cs/0008034},
year = {2007}
}
Comments
8 pages, uses acl2000.sty