  Most existing methods for biomedical entity recognition task rely on explicit
feature engineering where many features either are specific to a particular
task or depends on output of other existing NLP tools. Neural architectures
have been shown across various domains that efforts for explicit feature design
can be reduced. In this work we propose an unified framework using
bi-directional long short term memory network (BLSTM) for named entity
recognition (NER) tasks in biomedical and clinical domains. Three important
characteristics of the framework are as follows - (1) model learns contextual
as well as morphological features using two different BLSTM in hierarchy, (2)
model uses first order linear conditional random field (CRF) in its output
layer in cascade of BLSTM to infer label or tag sequence, (3) model does not
use any domain specific features or dictionary, i.e., in another words, same
set of features are used in the three NER tasks, namely, disease name
recognition (Disease NER), drug name recognition (Drug NER) and clinical entity
recognition (Clinical NER). We compare performance of the proposed model with
existing state-of-the-art models on the standard benchmark datasets of the
three tasks. We show empirically that the proposed framework outperforms all
existing models. Further our analysis of CRF layer and word-embedding obtained
using character based embedding show their importance.
