2000/08/30 by Stefan Riezler, Detlef Prescher, Jonas Kuhn +1
Computer Science · #cs.CL
published as Proceedings of the 38th Annual Meeting of the ACL, 2000 · 8 pages, uses acl2000.sty
arxiv created 2000/08/30 · arxiv updated 2009/11/30
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.