Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
The role of semantic roles in disambiguating verb senses
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A global joint model for semantic role labeling
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
Multilingual dependency-based syntactic and semantic parsing
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning: Shared Task
Joint unsupervised coreference resolution with Markov logic
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Word sense disambiguation using OntoNotes: an empirical study
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
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
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
Jointly identifying temporal relations with Markov Logic
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
A global model for joint lemmatization and part-of-speech prediction
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
An error-driven word-character hybrid model for joint Chinese word segmentation and POS 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
Joint learning of preposition senses and semantic roles of prepositional phrases
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
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
Joint models for Chinese POS tagging and dependency parsing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Enhancing search results with semantic annotation using augmented browsing
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Joint learning for coreference resolution with Markov logic
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Joint Optimization for Chinese POS Tagging and Dependency Parsing
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Semantic role labeling (SRL) and word sense disambiguation (WSD) are two fundamental tasks in natural language processing to find a sentence-level semantic representation. To date, they have mostly been modeled in isolation. However, this approach neglects logical constraints between them. We therefore exploit some pipeline systems which verify the automatic all word sense disambiguation could help the semantic role labeling and vice versa. We further propose a Markov logic model that jointly labels semantic roles and disambiguates all word senses. By evaluating our model on the OntoNotes 3.0 data, we show that this joint approach leads to a higher performance for word sense disambiguation and semantic role labeling than those pipeline approaches.