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Efficient inference on sequence segmentation models
ICML '06 Proceedings of the 23rd international conference on Machine learning
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ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
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ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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Semantic role labeling via integer linear programming inference
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Bidirectional inference with the easiest-first strategy for tagging sequence data
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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Expert Systems with Applications: An International Journal
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Pattern Recognition Letters
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Pattern Recognition
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ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
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Computer Speech and Language
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EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Generalized inference with multiple semantic role labeling systems
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Effective use of TimeBank for TimeML analysis
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UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Efficient and robust phrase chunking using support vector machines
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
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Voting between multiple data representations for text chunking
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MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
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CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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This paper describes a text chunking system based on a generalization of the Winnow algorithm. We propose a general statistical model for text chunking which we then convert into a classification problem. We argue that the Winnow family of algorithms is particularly suitable for solving classification problems arising from NLP applications, due to their robustness to irrelevant features. However in theory, Winnow may not converge for linearly non-separable data. To remedy this problem, we employ a generalization of the original Winnow method. An additional advantage of the new algorithm is that it provides reliable confidence estimates for its classification predictions. This property is required in our statistical modeling approach. We show that our system achieves state of the art performance in text chunking with less computational cost then previous systems.