Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Labeled pseudo-projective dependency parsing with support vector machines
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Semantic parsing for high-precision semantic role labelling
CoNLL '08 Proceedings of the Twelfth 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
Accurate parsing of the proposition bank
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
A latent variable model for generative dependency parsing
IWPT '07 Proceedings of the 10th International Conference on Parsing Technologies
Semantic parsing for high-precision semantic role labelling
CoNLL '08 Proceedings of the Twelfth 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
A latent variable model of synchronous syntactic-semantic parsing for multiple languages
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
A second-order joint eisner model for syntactic and semantic dependency parsing
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Online graph planarisation for synchronous parsing of semantic and syntactic dependencies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Analysis of discourse structure with syntactic dependencies and data-driven shift-reduce parsing
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Statistical bistratal dependency parsing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Incremental Sigmoid Belief Networks for Grammar Learning
The Journal of Machine Learning Research
Temporal restricted Boltzmann machines for dependency parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Scaling up automatic cross-lingual semantic role annotation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Transition-based semantic role labeling using predicate argument clustering
RELMS '11 Proceedings of the ACL 2011 Workshop on Relational Models of Semantics
A joint model for extended semantic role labeling
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Integrative semantic dependency parsing via efficient large-scale feature selection
Journal of Artificial Intelligence Research
Multilingual joint parsing of syntactic and semantic dependencies with a latent variable model
Computational Linguistics
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We propose a solution to the challenge of the CoNLL 2008 shared task that uses a generative history-based latent variable model to predict the most likely derivation of a synchronous dependency parser for both syntactic and semantic dependencies. The submitted model yields 79.1% macro-average F1 performance, for the joint task, 86.9% syntactic dependencies LAS and 71.0% semantic dependencies F1. A larger model trained after the deadline achieves 80.5% macro-average F1, 87.6% syntactic dependencies LAS, and 73.1% semantic dependencies F1.