Automatic labeling of semantic roles
Computational Linguistics
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A simple rule-based part of speech tagger
HLT '91 Proceedings of the workshop on Speech and Natural Language
Machine Learning
Joint learning improves semantic role labeling
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
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
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
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
Using document level cross-event inference to improve event extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Open-domain semantic role labeling by modeling word spans
ACL '10 Proceedings of the 48th Annual Meeting of the Association for 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
Two-stage NER for tweets with clustering
Information Processing and Management: an International Journal
Mining topic clouds from social data
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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As tweets have become a comprehensive repository of fresh information, Semantic Role Labeling (SRL) for tweets has aroused great research interests because of its central role in a wide range of tweet related studies such as fine-grained information extraction, sentiment analysis and summarization. However, the fact that a tweet is often too short and informal to provide sufficient information poses a major challenge. To tackle this challenge, we propose a new method to collectively label similar tweets. The underlying idea is to exploit similar tweets to make up for the lack of information in a tweet. Specifically, similar tweets are first grouped together by clustering. Then for each cluster a two-stage labeling is conducted: One labeler conducts SRL to get statistical information, such as the predicate/argument/role triples that occur frequently, from its highly confidently labeled results; then in the second stage, another labeler performs SRL with such statistical information to refine the results. Experimental results on a human annotated dataset show that our approach remarkably improves SRL by 3.1% F1.