  We present a simple approach to improve biomedical named entity recognition
(NER) by injecting categorical labels and Part-of-speech (POS) information into
the model. We use two approaches, in the first approach, we first train a
sequence-level classifier to classify the sentences into categories to obtain
the sentence-level tags (categorical labels). The sequence classifier is
modeled as an entailment problem by modifying the labels as a natural language
template. This helps to improve the accuracy of the classifier. Further, this
label information is injected into the NER model. In this paper, we demonstrate
effective ways to represent and inject these labels and POS attributes into the
NER model. In the second approach, we jointly learn the categorical labels and
NER labels. Here we also inject the POS tags into the model to increase the
syntactic context of the model. Experiments on three benchmark datasets show
that incorporating categorical label information with syntactic context is
quite useful and outperforms baseline BERT-based models.
