Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Information Retrieval
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
University of Sheffield: description of the LaSIE system as used for MUC-6
MUC6 '95 Proceedings of the 6th conference on Message understanding
PorTAL '02 Proceedings of the Third International Conference on Advances in Natural Language Processing
Recognition and Acquisition of Compound Names from Corpora
NLP '00 Proceedings of the Second International Conference on Natural Language Processing
Revision of Morphological Analysis Errors through the Person Name Construction Model
AMTA '98 Proceedings of the Third Conference of the Association for Machine Translation in the Americas on Machine Translation and the Information Soup
Using corpus-derived name lists for named entity recognition
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Bitext correspondences through rich mark-up
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Unsupervised learning of generalized names
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A WordNet-based approach to Named Entities recognition
SEMANET '02 Proceedings of the 2002 workshop on Building and using semantic networks - Volume 11
Implementing a sense tagger in a general architecture for text engineering
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
Proceedings of the first workshop on Information and knowledge management for developing region
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We describe an information extraction system in which four classes of naming expressions - organisation, person, location and time names - are recognised and classified with nearly 92% combined precision and recall. The system applies a mixture of techniques to perform this task and these are described in detail. We have quantitatively evaluated the system against a blind test set of Wall Street Journal business articles and report results not only for the system as a whole, but for each component technique and for each class of name. These results show that in order to have high recall, the system needs to make use not only of information internal to the naming expression but also information from outside the name. They also show that the contribution of each system component varies from one class of name expression to another.