Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
BEN: description of the PLUM system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
MITRE: description of the Alembic system used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
SRI International FASTUS system: MUC-6 test results and analysis
MUC6 '95 Proceedings of the 6th conference on Message understanding
Boosting trees for clause splitting
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Named Entity Extraction using AdaBoost
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
A resource-based method for named entity extraction and classification
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
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This paper presents a proposal for wide-coverage Named Entity-Extraction for Spanish. The extraction of named entities is treated using robust Machine Learning techniques (AdaBoost) and simple attributes requiring non-linguistically processed corpora, complemented with external information sources (a list of trigger words and a gazetteer). A thorough evaluation of the task on real corpora is presented in order to validate the appropriateness of the approach. The non linguistic nature of used features makes the approach easily portable to other languages.