Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
PCFG models of linguistic tree representations
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Parsing algorithms and metrics
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Recovering latent information in treebanks
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Probabilistic CFG with latent annotations
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
What to do when lexicalization fails: parsing German with suffix analysis and smoothing
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Inducing head-driven PCFGs with latent heads: refining a tree-bank grammar for parsing
ECML'05 Proceedings of the 16th European conference on Machine Learning
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
The PaGe 2008 shared task on parsing German
PaGe '08 Proceedings of the Workshop on Parsing German
Topological field parsing of German
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Scalable discriminative parsing for German
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Clause restructuring for SMT not absolutely helpful
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Joint Hebrew segmentation and parsing using a PCFG-LA lattice parser
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Data-driven parsing using probabilistic linear context-free rewriting systems
Computational Linguistics
Word segmentation, unknown-word resolution, and morphological agreement in a hebrew parsing system
Computational Linguistics
| Hi-index | 0.00 |
We describe experiments on learning latent variable grammars for various German tree-banks, using a language-agnostic statistical approach. In our method, a minimal initial grammar is hierarchically refined using an adaptive split-and-merge EM procedure, giving compact, accurate grammars. The learning procedure directly maximizes the likelihood of the training treebank, without the use of any language specific or linguistically constrained features. Nonetheless, the resulting grammars encode many linguistically interpretable patterns and give the best published parsing accuracies on three German treebanks.