  Biomedical Named Entity Recognition (BioNER) is a crucial step for analyzing
Biomedical texts, which aims at extracting biomedical named entities from a
given text. Different supervised machine learning algorithms have been applied
for BioNER by various researchers. The main requirement of these approaches is
an annotated dataset used for learning the parameters of machine learning
algorithms. Segment Representation (SR) models comprise of different tag sets
used for representing the annotated data, such as IOB2, IOE2 and IOBES. In this
paper, we propose an extension of IOBES model to improve the performance of
BioNER. The proposed SR model, FROBES, improves the representation of
multi-word entities. We used Bidirectional Long Short-Term Memory (BiLSTM)
network; an instance of Recurrent Neural Networks (RNN), to design a baseline
system for BioNER and evaluated the new SR model on two datasets, i2b2/VA 2010
challenge dataset and JNLPBA 2004 shared task dataset. The proposed SR model
outperforms other models for multi-word entities with length greater than two.
Further, the outputs of different SR models have been combined using majority
voting ensemble method which outperforms the baseline models performance.
