The syntactic process
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Immediate-head parsing for language models
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Identifying semantic roles using Combinatory Categorial Grammar
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Parsing the WSJ using CCG and log-linear models
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
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
The CoNLL-2009 shared task: syntactic and semantic dependencies in multiple languages
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Joint parsing and named entity recognition
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Brutus: a semantic role labeling system incorporating CCG, CFG, and dependency features
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
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In many NLP systems, there is a unidirectional flow of information in which a parser supplies input to a semantic role labeler. In this paper, we build a system that allows information to flow in both directions. We make use of semantic role predictions in choosing a single-best parse. This process relies on an averaged perceptron model to distinguish likely semantic roles from erroneous ones. Our system penalizes parses that give rise to low-scoring semantic roles. To explore the consequences of this we perform two experiments. First, we use a baseline generative model to produce n-best parses, which are then re-ordered by our semantic model. Second, we use a modified version of our semantic role labeler to predict semantic roles at parse time. The performance of this modified labeler is weaker than that of our best full SRL, because it is restricted to features that can be computed directly from the parser's packed chart. For both experiments, the resulting semantic predictions are then used to select parses. Finally, we feed the selected parses produced by each experiment to the full version of our semantic role labeler. We find that SRL performance can be improved over this baseline by selecting parses with likely semantic roles.