Abstract
Biomedical named entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. Exploiting unlabeled text data with a relatively small labeled corpus to build an accurate classification model has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. In this work, we proposed a new semi-supervised learning method based on self-training for biomedical named entity recognition. In this method, one classifier iteratively labels informative examples queried from the unlabeled data and learns on the most confident ones of them. Performance of the classifier is therefore improved. The proposed method outperforms the traditional self-training algorithm in terms of f-measure as well as, the number of training iterations performed to build a good classification model.
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Shin, E., Munkhdalai, T., Li, M., Paik, I., Ryu, K.H. (2012). A Self-training with Active Example Selection Criterion for Biomedical Named Entity Recognition. In: Lee, G., Howard, D., Kang, J.J., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Lecture Notes in Computer Science, vol 7425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32645-5_61
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DOI: https://doi.org/10.1007/978-3-642-32645-5_61
Publisher Name: Springer, Berlin, Heidelberg
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