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Nymble: a high-performance learning name-finder
Open accessAbstract
This paper presents a statistical, learned approach to finding names and other nonrecursive entities in text (as per the MUC-6 definition of the NE task), using a variant of the standard hidden Markov model. We present our justification for the problem and our approach, a detailed discussion of the model itself and finally the successful results of this new approach.
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A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010) · Machine Learning · doi:10.1007/s10994-009-5152-4 · Cited by 3,515
A survey of named entity recognition and classification
Nadeau, D., & Sekine, S. (2007) · Lingvisticae Investigationes · doi:10.1075/li.30.1.03nad · Cited by 2,507
Scaling question answering to the Web
Kwok, C. C. T., Etzioni, O., & Weld, D. S. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.371973 · Cited by 326
Information retrieval and text mining technologies for chemistry
Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00851 · Cited by 281
Metadata
- Authors
- Bikel, D. M., Miller, S., Schwartz, R., & Weischedel, R.
- Venue
- Proceedings of the fifth conference on Applied natural language processing -
- Published
- 1997
- Pages
- 194-201
- OpenAlex
- W2163918411
- Cited by
- 726
- References
- 0
- FWCI
- 24.56
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Nymble: a high-performance learning name-finder. https://app.cochat.ai/papers/10.3115/974557.974586
- The paper
- Bikel, D. M., Miller, S., Schwartz, R., & Weischedel, R. (1997). Nymble: a high-performance learning name-finder. Proceedings of the fifth conference on Applied natural language processing -. https://doi.org/10.3115/974557.974586
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
