Medical entity span extraction and linking are critical steps for many healthcare NLP tasks. Most existing entity extraction methods either have a fixed vocabulary of medical entities or require span annotations. In this paper, we propose a method for linking an open set of entities that does not require any span annotations. Our method, Open Set Label Attention Transformer (OSLAT), uses the label-attention mechanism to learn candidate-entity contextualized text representations. We find that OSLAT can not only link entities but is also able to implicitly learn spans associated with entities. We evaluate OSLAT on two tasks: (1) span extraction trained without explicit span annotations, and (2) entity linking trained without span-level annotation. We test the generalizability of our method by training two separate models on two datasets with low entity overlap and comparing cross-dataset performance.
@article{arxiv.2207.05817,
title = {OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction},
author = {Raymond Li and Ilya Valmianski and Li Deng and Xavier Amatriain and Anitha Kannan},
journal= {arXiv preprint arXiv:2207.05817},
year = {2022}
}
Comments
18 pages, 2 figures, Camera-Ready for ML4H 2022 (Proceedings Track)