Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Unsupervised learning of generalized names
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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
HLT '91 Proceedings of the workshop on Speech and Natural Language
Semi-supervised learning of geographical gazetteers from the internet
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
Bootstrapping toponym classifiers
HLT-NAACL-GEOREF '03 Proceedings of the HLT-NAACL 2003 workshop on Analysis of geographic references - Volume 1
Use of place information for improved event tracking
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Effective use of place information for event tracking
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
Heuristic methods for reducing errors of geographic named entities learned by bootstrapping
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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Geographic named entities can be classified into many sub-types that are useful for applications such as information extraction and question answering. In this paper, we present a bootstrapping algorithm for the task of geographic named entity annotation. In the initial stage, we annotate a raw corpus using seeds. From the initial annotation, boundary patterns are learned and applied to the corpus again to annotate new candidates. Type verification is adopted to reduce over-generation. One sense per discourse principle increases positive instances and also corrects mistaken annotations. As the bootstrapping loop proceeds, the annotated instances are increased gradually and the learned boundary patterns become gradually richer.