Lexicalized Stochastic Modeling of Constraint-Based Grammars using Log-Linear Measures and EM Training
Stefan Riezler, Detlef Prescher, Jonas Kuhn, Mark Johnson
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.
Create a lesson
Related papers
ToolSpace: Scaling Tool-Centric Environment Synthesis for Long-Horizon Agents
Guanting Dong, Zhichao Sun, Xinyu Yang et al.
Whose Effect Is It? How Objectives Inflate Learned Subspace Interventions
Toygar Tanyel, Serkan Ayvaz
Who is the Pronoun Pro? Comparing LLMs and Humans at Number-Disambiguation of 'they'
Carlos Hartmann
Shuffled OPSD: When Semantic Consistency Hurts On-Policy Self-Distillation?
Qingyuan Zheng, Zhihao Dou
HERIT: Enabling Global Access to Hanja-to-English Historical Translation by Mitigating Temporal Bias
Dayeon Jeong, Kyeongpil Kang
Residual Attribute Leakage Is an Interface: Auditing Privacy Evaluation After Text De-identification
Tuan Thanh Duong, Joongmin Shin, Jeongho Yoon et al.