Learning decision lists using homogeneous rules
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Unsupervised named entity recognition using syntactic and semantic contextual evidence
Computational Linguistics
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Unsupervised named entity classification models and their ensembles
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
HLT '91 Proceedings of the workshop on Speech and Natural Language
Training a named entity recognizer on the web
WISE'11 Proceedings of the 12th international conference on Web information system engineering
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A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. The bootstrapping procedure is implemented as training two successive learners. First, decision list is used to learn the parsing-based NE rules. Then, a Hidden Markov Model is trained on a corpus automatically tagged by the first learner. The resulting NE system approaches supervised NE performance for some NE types.