Finding a domain-appropriate sense inventory for semantically tagging a corpus
Natural Language Engineering
Automatic semantic tagging of unknown proper names
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Generalizing automatically generated selectional patterns
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
A "not-so-shallow" parser for collocational analysis
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
HLT '91 Proceedings of the workshop on Speech and Natural Language
Bootstrapping for named entity tagging using concept-based seeds
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
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
Prepositions in applications: A survey and introduction to the special issue
Computational Linguistics
Clique-based clustering for improving named entity recognition systems
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Automatic rule learning exploiting morphological features for named entity recognition in Turkish
Journal of Information Science
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts
Journal of Biomedical Informatics
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Proper nouns form an open class, making the incompleteness of manually or automatically learned classification rules an obvious problem. The purpose of this paper is twofold: first, to suggest the use of a complementary "backup" method to increase the robustness of any hand-crafted or machine-learning-based NE tagger; and second, to explore the effectiveness of using more fine-grained evidence--namely, syntactic and semantic contextual knowledge---in classifying NEs.