期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2015
卷号：8
期号：8
页码：279-288
DOI：10.14257/ijhit.2015.8.8.29
出版社：SERSC
摘要：Many machine learning methods have been applied on the biomedical named entity recognition and achieve good results on GENIA corpus. However most of those methods reply on the feature engineering which is labor-intensive. In this paper ， huge potential feature information represented as word vectors are generated by neutral networks based on unlabeled biomedical text files. We propose a Biomedical Named Entity Recognition (Bio-NER) method based on deep neural network architecture which has multiple layers and each layer abstracts features based upon the features generated by lower layers. Our system achieved F-score 71.01% on GENIA regular test corpus , F-score values for 5-fold cross-validation is 71.01% and this result is closed to the state-of-the-art performance with only POS (Part-of-speech) feature and represents the deep learning can effectively performed on biomedical NER.
关键词：Deep learning; Biomedical named entity recognition; Neutral networks
