Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
A unified architecture for natural language processing: deep neural networks with multitask learning
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A global joint model for 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
Dependency-based syntactic-semantic analysis with PropBank and NomBank
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Collective semantic role labelling with Markov logic
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
Multilingual semantic role labeling
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Multilingual semantic role labelling with Markov logic
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Jointly identifying predicates, arguments and senses using Markov logic
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Graph alignment for semi-supervised semantic role labeling
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Semi-supervised semantic role labeling using the latent words language model
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Statement map: reducing web information credibility noise through opinion classification
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Rich parameterization improves RNA structure prediction
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Leveraging Diverse Lexical Resources for Textual Entailment Recognition
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on RITE
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In predicate-argument structure analysis, it is important to capture non-local dependencies among arguments and inter-dependencies between the sense of a predicate and the semantic roles of its arguments. However, no existing approach explicitly handles both non-local dependencies and semantic dependencies between predicates and arguments. In this paper we propose a structured model that overcomes the limitation of existing approaches; the model captures both types of dependencies simultaneously by introducing four types of factors including a global factor type capturing non-local dependencies among arguments and a pairwise factor type capturing local dependencies between a predicate and an argument. In experiments the proposed model achieved competitive results compared to the state-of-the-art systems without applying any feature selection procedure.