Improving biomedical entity linking for complex entity mentions with LLM-based text simplification Article Swipe
Florian Borchert
,
Ignacio Llorca
,
Matthieu-P. Schapranow
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/database/baae067
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/database/baae067
Large amounts of important medical information are captured in free-text documents in biomedical research and within healthcare systems, which can be made accessible through natural language processing (NLP). A key component in most biomedical NLP pipelines is entity linking, i.e. grounding textual mentions of named entities to a reference of medical concepts, usually derived from a terminology system, such as the Systematized Nomenclature of Medicine Clinical Terms. However, complex entity mentions, spanning multiple tokens, are notoriously hard to normalize due to the difficulty of finding appropriate candidate concepts. In this work, we propose an approach to preprocess such mentions for candidate generation, building upon recent advances in text simplification with generative large language models. We evaluate the feasibility of our method in the context of the entity linking track of the BioCreative VIII SympTEMIST shared task. We find that instructing the latest Generative Pre-trained Transformer model with a few-shot prompt for text simplification results in mention spans that are easier to normalize. Thus, we can improve recall during candidate generation by 2.9 percentage points compared to our baseline system, which achieved the best score in the original shared task evaluation. Furthermore, we show that this improvement in recall can be fully translated into top-1 accuracy through careful initialization of a subsequent reranking model. Our best system achieves an accuracy of 63.6% on the SympTEMIST test set. The proposed approach has been integrated into the open-source xMEN toolkit, which is available online via https://github.com/hpi-dhc/xmen.
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Metadata
- Type
- article
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- en
- Landing Page
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All OpenAlex metadata
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https://openalex.org/W4401103265Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/database/baae067Digital Object Identifier
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Improving biomedical entity linking for complex entity mentions with LLM-based text simplificationWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-01-01Full publication date if available
- Authors
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Florian Borchert, Ignacio Llorca, Matthieu-P. SchapranowList of authors in order
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https://doi.org/10.1093/database/baae067Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1093/database/baae067Direct OA link when available
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Computer science, Information retrieval, Entity linking, Natural language processing, World Wide Web, Knowledge baseTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 7Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 41 |
| abstract_inverted_index.A | 29 |
| abstract_inverted_index.a | 48, 56, 148, 210 |
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| abstract_inverted_index.2.9 | 172 |
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| abstract_inverted_index.which | 19, 180, 238 |
| abstract_inverted_index.work, | 91 |
| abstract_inverted_index.(NLP). | 28 |
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| abstract_inverted_index.https://github.com/hpi-dhc/xmen. | 243 |
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