@inproceedings{otmakhova-etal-2020-improved,
title = "Improved Topic Representations of Medical Documents to Assist {COVID}-19 Literature Exploration",
author = "Otmakhova, Yulia and
Verspoor, Karin and
Baldwin, Timothy and
{\v{S}}uster, Simon",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Conway, Michael and
de Bruijn, Berry and
Dredze, Mark and
Mihalcea, Rada and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-2.12",
doi = "10.18653/v1/2020.nlpcovid19-2.12",
abstract = "Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and propose several ways to improve topic coherence and specificity.",
}
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%0 Conference Proceedings
%T Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration
%A Otmakhova, Yulia
%A Verspoor, Karin
%A Baldwin, Timothy
%A Šuster, Simon
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F otmakhova-etal-2020-improved
%X Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and propose several ways to improve topic coherence and specificity.
%R 10.18653/v1/2020.nlpcovid19-2.12
%U https://aclanthology.org/2020.nlpcovid19-2.12
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.12
Markdown (Informal)
[Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration](https://aclanthology.org/2020.nlpcovid19-2.12) (Otmakhova et al., NLP-COVID19 2020)
ACL