Background: Biomedical named entity recognition (Bio-NER) is a fundamental task in handling biomedical text terms, such as RNA, protein, cell type, cell line, and DNA. Bio-NER is one of the most elementary and core tasks in biomedical knowledge discovery from texts. The system described here is developed by using the BioNLP/NLPBA 2004 shared task. Experiments are conducted on a training and evaluation set provided by the task organizers. Results: Our results show that, compared with a baseline having a 70.09% F1 score, the RNN Jordan- and Elman-type algorithms have F1 scores of approximately 60.53% and 58.80%, respectively. When we use CRF as a machine learning algorithm, CCA, GloVe, and Word2Vec have F1 scores of 72.73%, 72.74%, and 72.82%, respectively. Conclusions: By using the word embedding constructed through the unsupervised learning, the time and cost required to construct the learning data can be saved.
CITATION STYLE
Song, H. J., Jo, B. C., Park, C. Y., Kim, J. D., & Kim, Y. S. (2018). Comparison of named entity recognition methodologies in biomedical documents. BioMedical Engineering Online, 17. https://doi.org/10.1186/s12938-018-0573-6
Mendeley helps you to discover research relevant for your work.