A statistical profile of the Named Entity task
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Mixed-initiative development of language processing systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Named entity recognition: a maximum entropy approach using global information
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A bootstrapping approach to named entity classification using successive learners
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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In modern biology, digitization of biosystematics publications is an important task. Extraction of taxonomic names from such documents is one of its major issues. This is because these names identify the various genera and species. This article reports on our experiences with learning techniques for this particular task. We say why established Named-Entity Recognition techniques are somewhat difficult to use in our context. One reason is that we have only very little training data available. Our experiments show that a combining approach that relies on regular expressions, heuristics, and word-level language recognition achieves very high precision and recall and allows to cope with those difficulties.