C4.5: programs for machine learning
C4.5: programs for machine learning
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Using text processing techniques to automatically enrich a domain ontology
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
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
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Fine-grained proper noun ontologies for question answering
SEMANET '02 Proceedings of the 2002 workshop on Building and using semantic networks - Volume 11
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
A robust risk minimization based named entity recognition system
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Improving machine translation quality with automatic named entity recognition
EAMT '03 Proceedings of the 7th International EAMT workshop on MT and other Language Technology Tools, Improving MT through other Language Technology Tools: Resources and Tools for Building MT
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In this work we present a method for Named Entity Recognition (NER). Our method does not rely on complex linguistic resources, and apart from a hand coded system, we do not use any language-dependent tools. The only information we use is automatically extracted from the documents, without human intervention. Moreover, the method performs well even without the use of the hand coded system. The experimental results are very encouraging. Our approach even outperformed the hand coded system on NER in Spanish, and it achieved high accuracies in Portuguese.