Making large-scale support vector machine learning practical
Advances in kernel methods
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A simple named entity extractor using AdaBoost
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Joint learning improves semantic role labeling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A robust combination strategy for semantic role labeling
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
SemEval-2007 task 09: multilevel semantic annotation of Catalan and Spanish
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Spanish Nested Named Entity Recognition Using a Syntax-Dependent Tree Traversal-Based Strategy
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
DeSRL: a linear-time semantic role labeling system
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
Nested named entity recognition
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
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This paper describes UPC's participation in the SemEval-2007 task 9 (Màrquez et al., 2007). We addressed all four subtasks using supervised learning. The paper introduces several novel issues: (a) for the SRL task, we propose a novel reranking algorithm based on the re-ranking Perceptron of Collins and Duffy (2002); and (b) for the same task we introduce a new set of global features that extract information not only at proposition level but also from the complete set of frame candidates. We show that in the SemEval setting, i.e., small training corpora, this approach outperforms previous work. Additionally, we added NSD and NER information in the global SRL model but this experiment was unsuccessful.