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        <title>Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning</title>
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        <namePart type="given">Rina</namePart>
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    <name type="personal">
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    <abstract>We study cross-lingual UMLS named entity linking, where mentions in a given source language are mapped to UMLS concepts, most of which are labeled in English. Our cross-lingual framework includes an offline unsupervised construction of a translated UMLS dictionary and a per-document pipeline which identifies UMLS candidate mentions and uses a fine-tuned pretrained transformer language model to filter candidates according to context. Our method exploits a small dataset of manually annotated UMLS mentions in the source language and uses this supervised data in two ways: to extend the unsupervised UMLS dictionary and to fine-tune the contextual filtering of candidate mentions in full documents. We demonstrate results of our approach on both Hebrew and English. We achieve new state-of-the-art (SOTA) results on the Hebrew Camoni corpus, +8.9 F1 on average across three communities in the dataset. We also achieve new SOTA on the English dataset MedMentions with +7.3 F1.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-acl.266</identifier>
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%0 Conference Proceedings
%T Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning
%A Galperin, Rina
%A Schnapp, Shachar
%A Elhadad, Michael
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F galperin-etal-2022-cross
%X We study cross-lingual UMLS named entity linking, where mentions in a given source language are mapped to UMLS concepts, most of which are labeled in English. Our cross-lingual framework includes an offline unsupervised construction of a translated UMLS dictionary and a per-document pipeline which identifies UMLS candidate mentions and uses a fine-tuned pretrained transformer language model to filter candidates according to context. Our method exploits a small dataset of manually annotated UMLS mentions in the source language and uses this supervised data in two ways: to extend the unsupervised UMLS dictionary and to fine-tune the contextual filtering of candidate mentions in full documents. We demonstrate results of our approach on both Hebrew and English. We achieve new state-of-the-art (SOTA) results on the Hebrew Camoni corpus, +8.9 F1 on average across three communities in the dataset. We also achieve new SOTA on the English dataset MedMentions with +7.3 F1.
%R 10.18653/v1/2022.findings-acl.266
%U https://aclanthology.org/2022.findings-acl.266
%U https://doi.org/10.18653/v1/2022.findings-acl.266
%P 3380-3390

Markdown (Informal)

[Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning](https://aclanthology.org/2022.findings-acl.266) (Galperin et al., Findings 2022)

• Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning (Galperin et al., Findings 2022)

ACL

• Rina Galperin, Shachar Schnapp, and Michael Elhadad. 2022. Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3380–3390, Dublin, Ireland. Association for Computational Linguistics.