Biomedical Nested NER with Large Language Model and UMLS Heuristics
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 7 Jul 2024.
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7 of 12 references.
References · 7
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2024Mixtral of Expertscited by 1,785
- 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
- 2019A Unified MRC Framework for Named Entity Recognitioncited by 199
- 2023Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Healthcited by 118
- 2019Nested Named Entity Recognition via Second-best Sequence Learning and Decodingcited by 34
- 2022NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entitiescited by 10
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