  Here we present the training and evaluation of NanoNER, a Named Entity
Recognition (NER) model for Nanobiology. NER consists in the identification of
specific entities in spans of unstructured texts and is often a primary task in
Natural Language Processing (NLP) and Information Extraction. The aim of our
model is to recognise entities previously identified by domain experts as
constituting the essential knowledge of the domain. Relying on ontologies,
which provide us with a domain vocabulary and taxonomy, we implemented an
iterative process enabling experts to determine the entities relevant to the
domain at hand. We then delve into the potential of distant supervision
learning in NER, supporting how this method can increase the quantity of
annotated data with minimal additional manpower. On our full corpus of 728
full-text nanobiology articles, containing more than 120k entity occurrences,
NanoNER obtained a F1-score of 0.98 on the recognition of previously known
entities. Our model also demonstrated its ability to discover new entities in
the text, with precision scores ranging from 0.77 to 0.81. Ablation experiments
further confirmed this and allowed us to assess the dependency of our approach
on the external resources. It highlighted the dependency of the approach to the
resource, while also confirming its ability to rediscover up to 30% of the
ablated terms. This paper details the methodology employed, experimental
design, and key findings, providing valuable insights and directions for future
related researches on NER in specialized domain. Furthermore, since our
approach require minimal manpower , we believe that it can be generalized to
other specialized fields.
