Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Prototype-driven learning for sequence models
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Baby SRL: modeling early language acquisition
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
A comparison of Bayesian estimators for unsupervised Hidden Markov Model POS taggers
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Minimized models for unsupervised part-of-speech tagging
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
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
Online latent structure training for language acquisition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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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. Each step depends on prior lexical and syntactic knowledge. Where do children learning their first languages begin in solving this problem? In this paper we focus on the parsing and argument-identification steps that precede Semantic Role Labeling (SRL) training. We combine a simplified SRL with an un-supervised HMM part of speech tagger, and experiment with psycholinguistically-motivated ways to label clusters resulting from the HMM so that they can be used to parse input for the SRL system. The results show that proposed shallow representations of sentence structure are robust to reductions in parsing accuracy, and that the contribution of alternative representations of sentence structure to successful semantic role labeling varies with the integrity of the parsing and argument-identification stages.