| --- | |
| language: en | |
| tags: | |
| - biomedical | |
| - lexical-semantics | |
| datasets: | |
| - UMLS | |
| **[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br> | |
| **[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**! | |
| ### SapBERT-PubMedBERT | |
| SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AA (English only), using [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) as the base model. Please use [CLS] as the representation of the input. | |
| ### Citation | |
| ```bibtex | |
| @inproceedings{liu-etal-2021-self, | |
| title = "Self-Alignment Pretraining for Biomedical Entity Representations", | |
| author = "Liu, Fangyu and | |
| Shareghi, Ehsan and | |
| Meng, Zaiqiao and | |
| Basaldella, Marco and | |
| Collier, Nigel", | |
| booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", | |
| month = jun, | |
| year = "2021", | |
| address = "Online", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/2021.naacl-main.334", | |
| pages = "4228--4238", | |
| abstract = "Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.", | |
| } | |
| ``` |