Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Supervised and unsupervised PCFG adaptation to novel domains
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Using predicate-argument structures for information extraction
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Introduction to Information Retrieval
Introduction to Information Retrieval
A global joint model for semantic role labeling
Computational Linguistics
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Semantic parsing for high-precision semantic role labelling
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
Collective semantic role labelling with Markov logic
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
Accurate parsing of the proposition bank
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
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Combination strategies for semantic role labeling
Journal of Artificial Intelligence Research
Semantic role labeling: past, present and future
ACLTutorials '09 Tutorial Abstracts of ACL-IJCNLP 2009
Semantic role labelling with tree conditional random fields
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Generalized inference with multiple semantic role labeling systems
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Semantic role labeling as sequential tagging
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
QuickView: advanced search of tweets
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Linguistic redundancy in Twitter
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
Learning from bullying traces in social media
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
QuickView: NLP-based tweet search
ACL '12 Proceedings of the ACL 2012 System Demonstrations
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News tweets that report what is happening have become an important real-time information source. We raise the problem of Semantic Role Labeling (SRL) for news tweets, which is meaningful for fine grained information extraction and retrieval. We present a self-supervised learning approach to train a domain specific SRL system to resolve the problem. A large volume of training data is automatically labeled, by leveraging the existing SRL system on news domain and content similarity between news and news tweets. On a human annotated test set, our system achieves state-of-the-art performance, outperforming the SRL system trained on news.