WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking
Afshin Rahimi, Timothy Baldwin, Karin Verspoor
Abstract
We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources. We propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1of 72%, a substantial improvement of 20% over word- and char-level BM25, enabling manual alignment with minimal effort. We release our resources, including ranked Wikipedia pages for 700k UMLSconcepts, and WikiUMLS, a dataset for training and evaluation of alignment models between UMLS and Wikipedia collected from Wikidata. This will provide easier access to Wikipedia for health professionals, patients, and NLP systems, including in multilingual settings.
BibTeX
@inproceedings{rahimi-etal-2020-wikiumls,
title = "{W}iki{UMLS}: Aligning {UMLS} to {W}ikipedia via Cross-lingual Neural Ranking",
author = "Rahimi, Afshin and
Baldwin, Timothy and
Verspoor, Karin",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.523/",
doi = "10.18653/v1/2020.coling-main.523",
pages = "5957--5962"
}