Application of Word Embeddings in Biomedical Named Entity Recognition Tasks 
F.X. Chang, J. Guo, W.R. Xu, S.Relly. Chung
Journal of Digital Information Management, Vol. 13, No. 5, 2015.
DOI: 
Link: http://dline.info/fpaper/jdim/v13i5/v13i5_2.pdf
Abstract:
Biomedical named entity recognition (Bio-
NER) is the fundamental task of biomedical text mining.
Machine-learning-based approaches, such as conditional
random fields (CRFs), have been widely applied in this
area, but the accuracy of these systems is limited
because of the finite annotated corpus. In this study, word
embedding features are generated from an unlabeled
corpus, which as extra word features are induced into the
CRFs system for Bio-NER. To further improved
performance, a post-processing algorithm is employed
after the named entity recognition task. Experimental
results show that the word embedding features generated
from a larger unlabeled corpus achieves higher
performance, and the use of word embedding features
increases F-measure on INLPBA04 data from 71.50% to
71.77%. After applying the post-processing algorithm, the
F-measure reaches 71.85%, which is superior to the
results in most existing systems.