Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Named entity recognition for Catalan using Spanish resources
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Combining data-driven systems for improving Named Entity Recognition
Data & Knowledge Engineering
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This work studies Named Entity Classification (NEC) for Catalan without making use of large annotated resources of this language. Two views are explored and compared, namely exploiting solely the Catalan resources, and a direct training of bilingual classification models (Spanish and Catalan), given that a large collection of annotated examples is available for Spanish. The empirical results obtained on real data point out that multilingual models clearly outperform monolingual ones, and that the resulting Catalan NEC models are easier to improve by bootstrapping on unlabelled data.