Tuning support vector machines for biomedical named entity recognition

@inproceedings{Kazama2002TuningSV,
  title={Tuning support vector machines for biomedical named entity recognition},
  author={Jun'ichi Kazama and Takaki Makino and Yoshihiro Ohta and Jun'ichi Tsujii},
  booktitle={ACL Workshop on Natural Language Processing in the Biomedical Domain},
  year={2002}
}
  • Jun'ichi Kazama, Takaki Makino, +1 author Jun'ichi Tsujii
  • Published in
    ACL Workshop on Natural…
    2002
  • Computer Science
  • We explore the use of Support Vector Machines (SVMs) for biomedical named entity recognition. To make the SVM training with the available largest corpus - the GENIA corpus - tractable, we propose to split the non-entity class into sub-classes, using part-of-speech information. In addition, we explore new features such as word cache and the states of an HMM trained by unsupervised learning. Experiments on the GENIA corpus show that our class splitting technique not only enables the training with… CONTINUE READING

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