  In recent years, with the growing amount of biomedical documents, coupled
with advancement in natural language processing algorithms, the research on
biomedical named entity recognition (BioNER) has increased exponentially.
However, BioNER research is challenging as NER in the biomedical domain are:
(i) often restricted due to limited amount of training data, (ii) an entity can
refer to multiple types and concepts depending on its context and, (iii) heavy
reliance on acronyms that are sub-domain specific. Existing BioNER approaches
often neglect these issues and directly adopt the state-of-the-art (SOTA)
models trained in general corpora which often yields unsatisfactory results. We
propose biomedical ALBERT (A Lite Bidirectional Encoder Representations from
Transformers for Biomedical Text Mining) bioALBERT, an effective
domain-specific language model trained on large-scale biomedical corpora
designed to capture biomedical context-dependent NER. We adopted a
self-supervised loss used in ALBERT that focuses on modelling inter-sentence
coherence to better learn context-dependent representations and incorporated
parameter reduction techniques to lower memory consumption and increase the
training speed in BioNER. In our experiments, BioALBERT outperformed
comparative SOTA BioNER models on eight biomedical NER benchmark datasets with
four different entity types. We trained four different variants of BioALBERT
models which are available for the research community to be used in future
research.
