An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Named-Entity Recognition from Greek and English Texts
Journal of Intelligent and Robotic Systems
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A System for Recognition of Named Entities in Greek
NLP '00 Proceedings of the Second International Conference on Natural Language Processing
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Machine learning-based named entity recognition via effective integration of various evidences
Natural Language Engineering
SRI International FASTUS system: MUC-6 test results and analysis
MUC6 '95 Proceedings of the 6th conference on Message understanding
Using machine learning to maintain rule-based named-entity recognition and classification systems
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Tuning support vector machines for biomedical named entity recognition
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Two-phase biomedical NE recognition based on SVMs
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Named entity recognition with a maximum entropy approach
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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We present a named-entity recognizer for Greek person names and temporal expressions. For temporal expressions, it relies on semi- automatically produced patterns. For person names, it employs two Support Vector Machines, that scan the input text in two passes, and active learning, which reduces the human annotation effort during training.