The necessity of parsing for predicate argument recognition
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Baby SRL: modeling early language acquisition
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
Minimally supervised model of early language acquisition
CoNLL '09 Proceedings of the Thirteenth 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
Discriminative learning over constrained latent representations
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Starting from scratch in semantic role labeling
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Statistical parsing with a context-free grammar and word statistics
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Crouching Dirichlet, hidden Markov model: unsupervised POS tagging with context local tag generation
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Illinois-Coref: the UI system in the CoNLL-2012 shared task
CoNLL '12 Joint Conference on EMNLP and CoNLL - Shared Task
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A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate arguments, and assign semantic roles to those constituents. Where do children learning their first languages begin in solving this problem? Even assuming children can derive a rough meaning for the sentence from the situation, how do they begin to map this meaning to the structure and the structure to the form of the sentence? In this paper we use feedback from a semantic role labeling (SRL) task to improve the intermediate syntactic representations that feed the SRL. We accomplish this by training an intermediate classifier using signals derived from latent structure optimization techniques. By using a separate classifier to predict internal structure we see benefits due to knowledge embedded in the classifier's feature representation. This extra structure allows the system to begin to learn using weaker, more plausible semantic feedback.