  The task of Named Entity Recognition (NER) is an important component of many
natural language processing systems, such as relation extraction and knowledge
graph construction. In this work, we present a simple and effective approach
for Named Entity Recognition. The main idea of our approach is to encode the
input subword sequence with a pre-trained transformer such as BERT, and then,
instead of directly classifying the word labels, another layer of transformer
is added to the subword representation to better encode the word-level
interaction. We evaluate our approach on three benchmark datasets for
scientific NER, particularly in the computer science and biomedical domains.
Experimental results show that our model outperforms the current
state-of-the-art on SciERC and TDM datasets without requiring external
resources or specific data augmentation. Code is available at
\url{https://github.com/urchade/HNER}.
