Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Three new probabilistic models for dependency parsing: an exploration
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Charting the depths of robust speech parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Reranking and self-training for parser adaptation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Hybrid learning of dependency structures from heterogeneous linguistic resources
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
When is self-training effective for parsing?
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Efficient HPSG parsing with supertagging and CFG-filtering
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
MAP adaptation of stochastic grammars
Computer Speech and Language
Enabling adaptation of lexicalised grammars to new domains
AdaptLRTtoND '09 Proceedings of the Workshop on Adaptation of Language Resources and Technology to New Domains
Grammar-driven versus data-driven: which parsing system is more affected by domain shifts?
NLPLING '10 Proceedings of the 2010 Workshop on NLP and Linguistics: Finding the Common Ground
Resolving speculation: MaxEnt cue classification and dependency-based scope rules
CoNLL '10: Shared Task Proceedings of the Fourteenth Conference on Computational Natural Language Learning --- Shared Task
Syntactic scope resolution in uncertainty analysis
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Minimally supervised domain-adaptive parse reranking for relation extraction
IWPT '11 Proceedings of the 12th International Conference on Parsing Technologies
Speculation and negation: Rules, rankers, and the role of syntax
Computational Linguistics
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Pure statistical parsing systems achieves high in-domain accuracy but performs poorly out-domain. In this paper, we propose two different approaches to produce syntactic dependency structures using a large-scale hand-crafted HPSG grammar. The dependency backbone of an HPSG analysis is used to provide general linguistic insights which, when combined with state-of-the-art statistical dependency parsing models, achieves performance improvements on out-domain tests.