@inproceedings{81e9fabbb251483eab358db88009632c,
title = "Disease Normalization with Graph Embeddings",
abstract = "The detection and normalization of diseases in biomedical texts are key biomedical natural language processing tasks. Disease names need not only be identified, but also normalized or linked to clinical taxonomies describing diseases such as MeSH{\textregistered}. In this paper we describe deep learning methods that tackle both tasks. We train and test our methods on the known NCBI disease benchmark corpus. We propose to represent disease names by leveraging MeSH{\textregistered} {\textquoteright}s graphical structure together with the lexical information available in the taxonomy using graph embeddings. We also show that combining neural named entity recognition models with our graph-based entity linking methods via multitask learning leads to improved disease recognition in the NCBI corpus.",
keywords = "biLSTM-CRF Models, Disease named entity normalization, Graph embeddings, Multi-task learning",
author = "D. Pujary and C. Thorne and W. Aziz",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; Intelligent Systems Conference, IntelliSys 2020 ; Conference date: 03-09-2020 Through 04-09-2020",
year = "2021",
doi = "10.1007/978-3-030-55187-2_18",
language = "Ingl{\'e}s",
isbn = "9783030551865",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "209--217",
editor = "Kohei Arai and Supriya Kapoor and Rahul Bhatia",
booktitle = "Intelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 2",
address = "Alemania",
}