Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for 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
Collective semantic role labelling with Markov logic
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Parsing syntactic and semantic dependencies with two single-stage maximum entropy models
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Applying sentence simplification to the CoNLL-2008 shared task
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Dependency-based semantic role labeling of PropBank
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Generalized inference with multiple semantic role labeling systems
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
A Markov logic approach to bio-molecular event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Improving semantic role labeling with word sense
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A structured model for joint learning of argument roles and predicate senses
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Jointly modeling WSD and SRL with Markov logic
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Semantic role labeling for news tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Collective semantic role labeling on open news corpus by leveraging redundancy
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Fast and robust joint models for biomedical event extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A joint model for extended semantic role labeling
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Collective semantic role labeling for tweets with clustering
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
QuickView: NLP-based tweet search
ACL '12 Proceedings of the ACL 2012 System Demonstrations
Improving NLP through marginalization of hidden syntactic structure
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Markov logic networks for situated incremental natural language understanding
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Integrative semantic dependency parsing via efficient large-scale feature selection
Journal of Artificial Intelligence Research
Situated incremental natural language understanding using Markov Logic Networks
Computer Speech and Language
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In this paper we present a Markov Logic Network for Semantic Role Labelling that jointly performs predicate identification, frame disambiguation, argument identification and argument classification for all predicates in a sentence. Empirically we find that our approach is competitive: our best model would appear on par with the best entry in the CoNLL 2008 shared task open track, and at the 4th place of the closed track---right behind the systems that use significantly better parsers to generate their input features. Moreover, we observe that by fully capturing the complete SRL pipeline in a single probabilistic model we can achieve significant improvements over more isolated systems, in particular for out-of-domain data. Finally, we show that despite the joint approach, our system is still efficient.