  Named entity recognition (NER) of chemicals and drugs is a critical domain of
information extraction in biochemical research. NER provides support for text
mining in biochemical reactions, including entity relation extraction,
attribute extraction, and metabolic response relationship extraction. However,
the existence of complex naming characteristics in the biomedical field, such
as polysemy and special characters, make the NER task very challenging. Here,
we propose a hybrid deep learning approach to improve the recognition accuracy
of NER. Specifically, our approach applies the Bidirectional Encoder
Representations from Transformers (BERT) model to extract the underlying
features of the text, learns a representation of the context of the text
through Bi-directional Long Short-Term Memory (BILSTM), and incorporates the
multi-head attention (MHATT) mechanism to extract chapter-level features. In
this approach, the MHATT mechanism aims to improve the recognition accuracy of
abbreviations to efficiently deal with the problem of inconsistency in
full-text labels. Moreover, conditional random field (CRF) is used to label
sequence tags because this probabilistic method does not need strict
independence assumptions and can accommodate arbitrary context information. The
experimental evaluation on a publicly-available dataset shows that the proposed
hybrid approach achieves the best recognition performance; in particular, it
substantially improves performance in recognizing abbreviations, polysemes, and
low-frequency entities, compared with the state-of-the-art approaches. For
instance, compared with the recognition accuracies for low-frequency entities
produced by the BILSTM-CRF algorithm, those produced by the hybrid approach on
two entity datasets (MULTIPLE and IDENTIFIER) have been increased by 80% and
21.69%, respectively.
