  Named entity recognition (NER) stands as a fundamental and pivotal task
within the realm of Natural Language Processing. Particularly within the domain
of Biomedical Method NER, this task presents notable challenges, stemming from
the continual influx of domain-specific terminologies in scholarly literature.
Current research in Biomedical Method (BioMethod) NER suffers from a scarcity
of resources, primarily attributed to the intricate nature of methodological
concepts, which necessitate a profound understanding for precise delineation.
In this study, we propose a novel dataset for biomedical method entity
recognition, employing an automated BioMethod entity recognition and
information retrieval system to assist human annotation. Furthermore, we
comprehensively explore a range of conventional and contemporary open-domain
NER methodologies, including the utilization of cutting-edge large-scale
language models (LLMs) customised to our dataset. Our empirical findings reveal
that the large parameter counts of language models surprisingly inhibit the
effective assimilation of entity extraction patterns pertaining to biomedical
methods. Remarkably, the approach, leveraging the modestly sized ALBERT model
(only 11MB), in conjunction with conditional random fields (CRF), achieves
state-of-the-art (SOTA) performance.
