A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Improving Term Extraction by System Combination Using Boosting
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Learning and Inference for Clause Identification
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Wide-Coverage Spanish Named Entity Extraction
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
The Journal of Machine Learning Research
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Filtering-Ranking Perceptron Learning for Partial Parsing
Machine Learning
Named Entity Extraction using AdaBoost
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Named entity recognition as a house of cards: classifier stacking
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
A simple named entity extractor using AdaBoost
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
The challenges of service-side personalized spam filtering: scalability and beyond
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Determining the Dependency Among Clauses Based on Machine Learning Techniques
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Introduction to the CoNLL-2001 shared task: clause identification
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Dependency Analysis of Clauses Using Parse Tree Kernels
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Clause boundary recognition using support vector machines
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Clause boundary identification using conditional random fields
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Comparison of various machine learning-based classifications of relative clauses
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
A machine learning approach to Portuguese clause identification
PROPOR'10 Proceedings of the 9th international conference on Computational Processing of the Portuguese Language
Email categorization with tournament methods
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Near-Duplicate mail detection based on URL information for spam filtering
ICOIN'06 Proceedings of the 2006 international conference on Information Networking: advances in Data Communications and Wireless Networks
Chinese event descriptive clause splitting with structured SVMs
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Using LazyBoosting for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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We present a system for the CoNLL-2001 shared task: the clause splitting problem. Our approach consists in decomposing the clause splitting problem into a combination of binary "simple" decisions, which we solve with the AdaBoost learning algorithm. The whole problem is decomposed in two levels, with two chained decisions per level. The first level corresponds to parts 1 and 2 presented in the introductory document for the task. The second level corresponds to the part 3, which we decompose in two decisions and a combination procedure.