Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Boosted decision graphs for NLP learning tasks
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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
Meta-learning orthographic and contextual models for language independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition using a character-based probabilistic approach
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
STEWARD: architecture of a spatio-textual search engine
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
A new parallel association rule mining algorithm on distributed shared memory system
International Journal of Business Intelligence and Data Mining
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The Sydney Language Independent Named Entity Recogniser and Classifier (SLINERC) is a multi-stage system for the recognition and classification of named entities. Each stage uses a decision graph learner to combine statistical features with results from prior stages. Earlier stages are focused upon entity recognition, the division of non-entity terms from entities. Later stages concentrate on the classification of these entities into the desired classes. The best over-all f-values are 73.92 and 71.36 for the Spanish and Dutch datasets, respectively.