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.- Anthology ID:
- 2022.findings-acl.266
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3380–3390
- Language:
- URL:
- https://aclanthology.org/2022.findings-acl.266
- DOI:
- 10.18653/v1/2022.findings-acl.266
- Cite (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.
- Cite (Informal):
- Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning (Galperin et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-acl.266.pdf
- Code
- rinagalperin/biomedical_nel
- Data
- BC5CDR, MedMentions