Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
Combining data-driven systems for improving named entity recognition
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Combining data-driven systems for improving Named Entity Recognition
Data & Knowledge Engineering
Named entity recognition for Ukrainian: a resource-light approach
ACL '07 Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies
Bootstrapping named entity recognition with automatically generated gazetteer lists
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Combining labeled and unlabeled data with word-class distribution learning
Proceedings of the 18th ACM conference on Information and knowledge management
Training a named entity recognizer on the web
WISE'11 Proceedings of the 12th international conference on Web information system engineering
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The paper discusses the usage of unlabeled data for Spanish Named Entity Recognition. Two techniques have been used: self-training for detecting the entities in the text and co-training for classifying these already detected entities. We introduce a new co-training algorithm, which applies voting techniques in order to decide which unlabeled example should be added into the training set at each iteration. A proposal for improving the performance of the detected entities has been made. A brief comparative study with already existing co-training algorithms is demonstrated.