Paper: Recognizing Names In Biomedical Texts Using Hidden Markov Model And SVM Plus Sigmoid

ACL ID W04-1201
Title Recognizing Names In Biomedical Texts Using Hidden Markov Model And SVM Plus Sigmoid
Venue International Joint Workshop On Natural Language Processing In Biomedicine And Its Applications NLPBA BioNLP
Session
Year 2004
Authors

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 GE...