  In this paper, we present our system for the BioNNE English track, which aims
to extract 8 types of biomedical nested named entities from biomedical text. We
use a large language model (Mixtral 8x7B instruct) and ScispaCy NER model to
identify entities in an article and build custom heuristics based on unified
medical language system (UMLS) semantic types to categorize the entities. We
discuss the results and limitations of our system and propose future
improvements. Our system achieved an F1 score of 0.39 on the BioNNE validation
set and 0.348 on the test set.
