The nature of statistical learning theory
The nature of statistical learning theory
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Named entity recognition using an HMM-based chunk tagger
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Tuning support vector machines for biomedical named entity recognition
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Two-phase biomedical NE recognition based on SVMs
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Boosting precision and recall of dictionary-based protein name recognition
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Effective adaptation of a Hidden Markov Model-based named entity recognizer for biomedical domain
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
The GENIA corpus: an annotated research abstract corpus in molecular biology domain
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach
International Journal of Bioinformatics Research and Applications
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In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena in the biomedical domain, various evidential features are proposed and integrated through a Hidden Markov Model (HMM). In addition, a Support Vector Machine (SVM) plus sigmoid is proposed to resolve the data sparseness problem in our system. Finally, we present two post-processing modules to deal with the cascaded entity name and abbreviation phenomena. Evaluation shows that our system achieves the F-measure of 69.1 and 71.2 on the 23 classes of GENIA V1.1 and V3.0 respectively. In particular, our system achieves the F-measure of 77.8 on the "protein" class of GENIA V3.0. It shows that our system outperforms the best published system on GENIA V1.1 and V3.0.