@inproceedings{grujicic-etal-2020-self,
title = "Self-supervised context-aware {COVID-19} document exploration through atlas grounding",
author = "Grujicic, Dusan and
Radevski, Gorjan and
Tuytelaars, Tinne and
Blaschko, Matthew",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Dredze, Mark and
Ferrara, Emilio and
May, Jonathan and
Munro, Robert and
Paris, Cecile and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID-19} at {ACL} 2020",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-acl.5",
abstract = "In this paper, we aim to develop a self-supervised grounding of Covid-related medical text based on the actual spatial relationships between the referred anatomical concepts. More specifically, we learn to project sentences into a physical space defined by a three-dimensional anatomical atlas, allowing for a visual approach to navigating Covid-related literature. We design a straightforward and empirically effective training objective to reduce the curated data dependency issue. We use BERT as the main building block of our model and perform a quantitative analysis that demonstrates that the model learns a context-aware mapping. We illustrate two potential use-cases for our approach, one in interactive, 3D data exploration, and the other in document retrieval. To accelerate research in this direction, we make public all trained models, codebase and the developed tools, which can be accessed at \url{https://github.com/gorjanradevski/macchina/}.",
}
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%0 Conference Proceedings
%T Self-supervised context-aware COVID-19 document exploration through atlas grounding
%A Grujicic, Dusan
%A Radevski, Gorjan
%A Tuytelaars, Tinne
%A Blaschko, Matthew
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Dredze, Mark
%Y Ferrara, Emilio
%Y May, Jonathan
%Y Munro, Robert
%Y Paris, Cecile
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F grujicic-etal-2020-self
%X In this paper, we aim to develop a self-supervised grounding of Covid-related medical text based on the actual spatial relationships between the referred anatomical concepts. More specifically, we learn to project sentences into a physical space defined by a three-dimensional anatomical atlas, allowing for a visual approach to navigating Covid-related literature. We design a straightforward and empirically effective training objective to reduce the curated data dependency issue. We use BERT as the main building block of our model and perform a quantitative analysis that demonstrates that the model learns a context-aware mapping. We illustrate two potential use-cases for our approach, one in interactive, 3D data exploration, and the other in document retrieval. To accelerate research in this direction, we make public all trained models, codebase and the developed tools, which can be accessed at https://github.com/gorjanradevski/macchina/.
%U https://aclanthology.org/2020.nlpcovid19-acl.5
Markdown (Informal)
[Self-supervised context-aware COVID-19 document exploration through atlas grounding](https://aclanthology.org/2020.nlpcovid19-acl.5) (Grujicic et al., NLP-COVID19 2020)
ACL